Response Surface

Linear Kernel

  • A9A Data Set Name
  • 2 Number of Classes
  • 52098/13025 Number of Instances (train/test)
  • 83 Number of Attributes

Response Surface

Polynomial Kernel

  • A9A Data Set Name
  • 2 Number of Classes
  • 52098/13025 Number of Instances (train/test)
  • 83 Number of Attributes

Response Surface

RBF-Kernel

  • A9A Data Set Name
  • 2 Number of Classes
  • 52098/13025 Number of Instances (train/test)
  • 83 Number of Attributes

Response Surface

Linear Kernel

  • abalone Data Set Name
  • 28 Number of Classes
  • 3339/835 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • abalone Data Set Name
  • 28 Number of Classes
  • 3339/835 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • abalone Data Set Name
  • 28 Number of Classes
  • 3339/835 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • acoustic Data Set Name
  • 3 Number of Classes
  • 94586/23647 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

Polynomial Kernel

  • acoustic Data Set Name
  • 3 Number of Classes
  • 94586/23647 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

RBF-Kernel

  • acoustic Data Set Name
  • 3 Number of Classes
  • 94586/23647 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

Linear Kernel

  • aloi Data Set Name
  • 1000 Number of Classes
  • 86400/21600 Number of Instances (train/test)
  • 128 Number of Attributes

Response Surface

Polynomial Kernel

  • aloi Data Set Name
  • 1000 Number of Classes
  • 86400/21600 Number of Instances (train/test)
  • 128 Number of Attributes

Response Surface

RBF-Kernel

  • aloi Data Set Name
  • 1000 Number of Classes
  • 86400/21600 Number of Instances (train/test)
  • 128 Number of Attributes

Response Surface

Linear Kernel

  • appendicitis Data Set Name
  • 2 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

Polynomial Kernel

  • appendicitis Data Set Name
  • 2 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

RBF-Kernel

  • appendicitis Data Set Name
  • 2 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

Linear Kernel

  • australian Data Set Name
  • 2 Number of Classes
  • 552/138 Number of Instances (train/test)
  • 14 Number of Attributes

Response Surface

Polynomial Kernel

  • australian Data Set Name
  • 2 Number of Classes
  • 552/138 Number of Instances (train/test)
  • 14 Number of Attributes

Response Surface

RBF-Kernel

  • australian Data Set Name
  • 2 Number of Classes
  • 552/138 Number of Instances (train/test)
  • 14 Number of Attributes

Response Surface

Linear Kernel

  • automobile Data Set Name
  • 6 Number of Classes
  • 127/32 Number of Instances (train/test)
  • 75 Number of Attributes

Response Surface

Polynomial Kernel

  • automobile Data Set Name
  • 6 Number of Classes
  • 127/32 Number of Instances (train/test)
  • 75 Number of Attributes

Response Surface

RBF-Kernel

  • automobile Data Set Name
  • 6 Number of Classes
  • 127/32 Number of Instances (train/test)
  • 75 Number of Attributes

Response Surface

Linear Kernel

  • balance Data Set Name
  • 3 Number of Classes
  • 500/125 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Polynomial Kernel

  • balance Data Set Name
  • 3 Number of Classes
  • 500/125 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

RBF-Kernel

  • balance Data Set Name
  • 3 Number of Classes
  • 500/125 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Linear Kernel

  • banana Data Set Name
  • 2 Number of Classes
  • 4240/1060 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

Polynomial Kernel

  • banana Data Set Name
  • 2 Number of Classes
  • 4240/1060 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

RBF-Kernel

  • banana Data Set Name
  • 2 Number of Classes
  • 4240/1060 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

Linear Kernel

  • bands Data Set Name
  • 2 Number of Classes
  • 292/73 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Polynomial Kernel

  • bands Data Set Name
  • 2 Number of Classes
  • 292/73 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

RBF-Kernel

  • bands Data Set Name
  • 2 Number of Classes
  • 292/73 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Linear Kernel

  • breast-cancer Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • breast-cancer Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • breast-cancer Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • bupa Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Polynomial Kernel

  • bupa Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

RBF-Kernel

  • bupa Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Linear Kernel

  • car Data Set Name
  • 4 Number of Classes
  • 1382/346 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • car Data Set Name
  • 4 Number of Classes
  • 1382/346 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • car Data Set Name
  • 4 Number of Classes
  • 1382/346 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • census Data Set Name
  • 2 Number of Classes
  • 114016/28505 Number of Instances (train/test)
  • 396 Number of Attributes

Response Surface

Polynomial Kernel

  • census Data Set Name
  • 2 Number of Classes
  • 114016/28505 Number of Instances (train/test)
  • 396 Number of Attributes

Response Surface

RBF-Kernel

  • census Data Set Name
  • 2 Number of Classes
  • 114016/28505 Number of Instances (train/test)
  • 396 Number of Attributes

Response Surface

Linear Kernel

  • chess Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 72 Number of Attributes

Response Surface

Polynomial Kernel

  • chess Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 72 Number of Attributes

Response Surface

RBF-Kernel

  • chess Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 72 Number of Attributes

Response Surface

Linear Kernel

  • cleveland Data Set Name
  • 5 Number of Classes
  • 237/60 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Polynomial Kernel

  • cleveland Data Set Name
  • 5 Number of Classes
  • 237/60 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

RBF-Kernel

  • cleveland Data Set Name
  • 5 Number of Classes
  • 237/60 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Linear Kernel

  • cmc Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • cmc Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • cmc Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • cod-rna Data Set Name
  • 2 Number of Classes
  • 264921/66231 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • cod-rna Data Set Name
  • 2 Number of Classes
  • 264921/66231 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • cod-rna Data Set Name
  • 2 Number of Classes
  • 264921/66231 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • coil2000 Data Set Name
  • 2 Number of Classes
  • 7857/1965 Number of Instances (train/test)
  • 80 Number of Attributes

Response Surface

Polynomial Kernel

  • coil2000 Data Set Name
  • 2 Number of Classes
  • 7857/1965 Number of Instances (train/test)
  • 80 Number of Attributes

Response Surface

RBF-Kernel

  • coil2000 Data Set Name
  • 2 Number of Classes
  • 7857/1965 Number of Instances (train/test)
  • 80 Number of Attributes

Response Surface

Linear Kernel

  • colon-cancer Data Set Name
  • 2 Number of Classes
  • 49/13 Number of Instances (train/test)
  • 2000 Number of Attributes

Response Surface

Polynomial Kernel

  • colon-cancer Data Set Name
  • 2 Number of Classes
  • 49/13 Number of Instances (train/test)
  • 2000 Number of Attributes

Response Surface

RBF-Kernel

  • colon-cancer Data Set Name
  • 2 Number of Classes
  • 49/13 Number of Instances (train/test)
  • 2000 Number of Attributes

Response Surface

Linear Kernel

  • connect-4 Data Set Name
  • 3 Number of Classes
  • 54045/13512 Number of Instances (train/test)
  • 125 Number of Attributes

Response Surface

Polynomial Kernel

  • connect-4 Data Set Name
  • 3 Number of Classes
  • 54045/13512 Number of Instances (train/test)
  • 125 Number of Attributes

Response Surface

RBF-Kernel

  • connect-4 Data Set Name
  • 3 Number of Classes
  • 54045/13512 Number of Instances (train/test)
  • 125 Number of Attributes

Response Surface

Linear Kernel

  • contraceptive Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Polynomial Kernel

  • contraceptive Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

RBF-Kernel

  • contraceptive Data Set Name
  • 3 Number of Classes
  • 1178/295 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Linear Kernel

  • covtypem Data Set Name
  • 7 Number of Classes
  • 464809/116203 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Polynomial Kernel

  • covtypem Data Set Name
  • 7 Number of Classes
  • 464809/116203 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

RBF-Kernel

  • covtypem Data Set Name
  • 7 Number of Classes
  • 464809/116203 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Linear Kernel

  • credit-g Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • credit-g Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • credit-g Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • crx Data Set Name
  • 2 Number of Classes
  • 522/131 Number of Instances (train/test)
  • 45 Number of Attributes

Response Surface

Polynomial Kernel

  • crx Data Set Name
  • 2 Number of Classes
  • 522/131 Number of Instances (train/test)
  • 45 Number of Attributes

Response Surface

RBF-Kernel

  • crx Data Set Name
  • 2 Number of Classes
  • 522/131 Number of Instances (train/test)
  • 45 Number of Attributes

Response Surface

Linear Kernel

  • dermatology Data Set Name
  • 6 Number of Classes
  • 286/72 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

Polynomial Kernel

  • dermatology Data Set Name
  • 6 Number of Classes
  • 286/72 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

RBF-Kernel

  • dermatology Data Set Name
  • 6 Number of Classes
  • 286/72 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

Linear Kernel

  • diabetes Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • diabetes Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • diabetes Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • dna Data Set Name
  • 3 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 180 Number of Attributes

Response Surface

Polynomial Kernel

  • dna Data Set Name
  • 3 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 180 Number of Attributes

Response Surface

RBF-Kernel

  • dna Data Set Name
  • 3 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 180 Number of Attributes

Response Surface

Linear Kernel

  • duke-breast-cancer Data Set Name
  • 2 Number of Classes
  • 35/9 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

Polynomial Kernel

  • duke-breast-cancer Data Set Name
  • 2 Number of Classes
  • 35/9 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

RBF-Kernel

  • duke-breast-cancer Data Set Name
  • 2 Number of Classes
  • 35/9 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

Linear Kernel

  • ecoli Data Set Name
  • 8 Number of Classes
  • 268/68 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

Polynomial Kernel

  • ecoli Data Set Name
  • 8 Number of Classes
  • 268/68 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

RBF-Kernel

  • ecoli Data Set Name
  • 8 Number of Classes
  • 268/68 Number of Instances (train/test)
  • 7 Number of Attributes

Response Surface

Linear Kernel

  • factors Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 216 Number of Attributes

Response Surface

Polynomial Kernel

  • factors Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 216 Number of Attributes

Response Surface

RBF-Kernel

  • factors Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 216 Number of Attributes

Response Surface

Linear Kernel

  • flare Data Set Name
  • 6 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Polynomial Kernel

  • flare Data Set Name
  • 6 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

RBF-Kernel

  • flare Data Set Name
  • 6 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Linear Kernel

  • fourclass Data Set Name
  • 2 Number of Classes
  • 689/173 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

Polynomial Kernel

  • fourclass Data Set Name
  • 2 Number of Classes
  • 689/173 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

RBF-Kernel

  • fourclass Data Set Name
  • 2 Number of Classes
  • 689/173 Number of Instances (train/test)
  • 2 Number of Attributes

Response Surface

Linear Kernel

  • fourier Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 76 Number of Attributes

Response Surface

Polynomial Kernel

  • fourier Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 76 Number of Attributes

Response Surface

RBF-Kernel

  • fourier Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 76 Number of Attributes

Response Surface

Linear Kernel

  • german-numer Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 23 Number of Attributes

Response Surface

Polynomial Kernel

  • german-numer Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 23 Number of Attributes

Response Surface

RBF-Kernel

  • german-numer Data Set Name
  • 2 Number of Classes
  • 800/200 Number of Instances (train/test)
  • 23 Number of Attributes

Response Surface

Linear Kernel

  • gisette Data Set Name
  • 2 Number of Classes
  • 5600/1400 Number of Instances (train/test)
  • 4996 Number of Attributes

Response Surface

Polynomial Kernel

  • gisette Data Set Name
  • 2 Number of Classes
  • 5600/1400 Number of Instances (train/test)
  • 4996 Number of Attributes

Response Surface

RBF-Kernel

  • gisette Data Set Name
  • 2 Number of Classes
  • 5600/1400 Number of Instances (train/test)
  • 4996 Number of Attributes

Response Surface

Linear Kernel

  • haberman Data Set Name
  • 2 Number of Classes
  • 244/62 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Polynomial Kernel

  • haberman Data Set Name
  • 2 Number of Classes
  • 244/62 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

RBF-Kernel

  • haberman Data Set Name
  • 2 Number of Classes
  • 244/62 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Linear Kernel

  • hayes-roth Data Set Name
  • 3 Number of Classes
  • 128/32 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Polynomial Kernel

  • hayes-roth Data Set Name
  • 3 Number of Classes
  • 128/32 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

RBF-Kernel

  • hayes-roth Data Set Name
  • 3 Number of Classes
  • 128/32 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Linear Kernel

  • heart Data Set Name
  • 2 Number of Classes
  • 216/54 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Polynomial Kernel

  • heart Data Set Name
  • 2 Number of Classes
  • 216/54 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

RBF-Kernel

  • heart Data Set Name
  • 2 Number of Classes
  • 216/54 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Linear Kernel

  • hepatitis Data Set Name
  • 2 Number of Classes
  • 64/16 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

Polynomial Kernel

  • hepatitis Data Set Name
  • 2 Number of Classes
  • 64/16 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

RBF-Kernel

  • hepatitis Data Set Name
  • 2 Number of Classes
  • 64/16 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

Linear Kernel

  • housevotes Data Set Name
  • 2 Number of Classes
  • 185/47 Number of Instances (train/test)
  • 31 Number of Attributes

Response Surface

Polynomial Kernel

  • housevotes Data Set Name
  • 2 Number of Classes
  • 185/47 Number of Instances (train/test)
  • 31 Number of Attributes

Response Surface

RBF-Kernel

  • housevotes Data Set Name
  • 2 Number of Classes
  • 185/47 Number of Instances (train/test)
  • 31 Number of Attributes

Response Surface

Linear Kernel

  • ijcnn1 Data Set Name
  • 2 Number of Classes
  • 101360/25341 Number of Instances (train/test)
  • 22 Number of Attributes

Response Surface

Polynomial Kernel

  • ijcnn1 Data Set Name
  • 2 Number of Classes
  • 101360/25341 Number of Instances (train/test)
  • 22 Number of Attributes

Response Surface

RBF-Kernel

  • ijcnn1 Data Set Name
  • 2 Number of Classes
  • 101360/25341 Number of Instances (train/test)
  • 22 Number of Attributes

Response Surface

Linear Kernel

  • ionosphere Data Set Name
  • 2 Number of Classes
  • 280/71 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

Polynomial Kernel

  • ionosphere Data Set Name
  • 2 Number of Classes
  • 280/71 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

RBF-Kernel

  • ionosphere Data Set Name
  • 2 Number of Classes
  • 280/71 Number of Instances (train/test)
  • 34 Number of Attributes

Response Surface

Linear Kernel

  • iris Data Set Name
  • 3 Number of Classes
  • 120/30 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Polynomial Kernel

  • iris Data Set Name
  • 3 Number of Classes
  • 120/30 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

RBF-Kernel

  • iris Data Set Name
  • 3 Number of Classes
  • 120/30 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Linear Kernel

  • karhunen Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 64 Number of Attributes

Response Surface

Polynomial Kernel

  • karhunen Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 64 Number of Attributes

Response Surface

RBF-Kernel

  • karhunen Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 64 Number of Attributes

Response Surface

Linear Kernel

  • kddcup Data Set Name
  • 23 Number of Classes
  • 395216/98804 Number of Instances (train/test)
  • 138 Number of Attributes

Response Surface

Polynomial Kernel

  • kddcup Data Set Name
  • 23 Number of Classes
  • 395216/98804 Number of Instances (train/test)
  • 138 Number of Attributes

Response Surface

RBF-Kernel

  • kddcup Data Set Name
  • 23 Number of Classes
  • 395216/98804 Number of Instances (train/test)
  • 138 Number of Attributes

Response Surface

Linear Kernel

  • kr-vs-k Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

Polynomial Kernel

  • kr-vs-k Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

RBF-Kernel

  • kr-vs-k Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

Linear Kernel

  • kr-vs-kp Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

Polynomial Kernel

  • kr-vs-kp Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

RBF-Kernel

  • kr-vs-kp Data Set Name
  • 2 Number of Classes
  • 2556/640 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

Linear Kernel

  • kropt Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Polynomial Kernel

  • kropt Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

RBF-Kernel

  • kropt Data Set Name
  • 18 Number of Classes
  • 22444/5612 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Linear Kernel

  • led7digit Data Set Name
  • 10 Number of Classes
  • 400/100 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Polynomial Kernel

  • led7digit Data Set Name
  • 10 Number of Classes
  • 400/100 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

RBF-Kernel

  • led7digit Data Set Name
  • 10 Number of Classes
  • 400/100 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Linear Kernel

  • letter Data Set Name
  • 26 Number of Classes
  • 12000/3000 Number of Instances (train/test)
  • 16 Number of Attributes

Response Surface

Polynomial Kernel

  • letter Data Set Name
  • 26 Number of Classes
  • 12000/3000 Number of Instances (train/test)
  • 16 Number of Attributes

Response Surface

RBF-Kernel

  • letter Data Set Name
  • 26 Number of Classes
  • 12000/3000 Number of Instances (train/test)
  • 16 Number of Attributes

Response Surface

Linear Kernel

  • leukemia Data Set Name
  • 2 Number of Classes
  • 57/15 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

Polynomial Kernel

  • leukemia Data Set Name
  • 2 Number of Classes
  • 57/15 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

RBF-Kernel

  • leukemia Data Set Name
  • 2 Number of Classes
  • 57/15 Number of Instances (train/test)
  • 7129 Number of Attributes

Response Surface

Linear Kernel

  • liver-disorders Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Polynomial Kernel

  • liver-disorders Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

RBF-Kernel

  • liver-disorders Data Set Name
  • 2 Number of Classes
  • 276/69 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Linear Kernel

  • lymphography Data Set Name
  • 3 Number of Classes
  • 118/30 Number of Instances (train/test)
  • 46 Number of Attributes

Response Surface

Polynomial Kernel

  • lymphography Data Set Name
  • 3 Number of Classes
  • 118/30 Number of Instances (train/test)
  • 46 Number of Attributes

Response Surface

RBF-Kernel

  • lymphography Data Set Name
  • 3 Number of Classes
  • 118/30 Number of Instances (train/test)
  • 46 Number of Attributes

Response Surface

Linear Kernel

  • magic Data Set Name
  • 2 Number of Classes
  • 15216/3804 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • magic Data Set Name
  • 2 Number of Classes
  • 15216/3804 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • magic Data Set Name
  • 2 Number of Classes
  • 15216/3804 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • mammographic Data Set Name
  • 2 Number of Classes
  • 664/166 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Polynomial Kernel

  • mammographic Data Set Name
  • 2 Number of Classes
  • 664/166 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

RBF-Kernel

  • mammographic Data Set Name
  • 2 Number of Classes
  • 664/166 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Linear Kernel

  • marketing Data Set Name
  • 9 Number of Classes
  • 5500/1376 Number of Instances (train/test)
  • 12 Number of Attributes

Response Surface

Polynomial Kernel

  • marketing Data Set Name
  • 9 Number of Classes
  • 5500/1376 Number of Instances (train/test)
  • 12 Number of Attributes

Response Surface

RBF-Kernel

  • marketing Data Set Name
  • 9 Number of Classes
  • 5500/1376 Number of Instances (train/test)
  • 12 Number of Attributes

Response Surface

Linear Kernel

  • mnist Data Set Name
  • 10 Number of Classes
  • 56000/14000 Number of Instances (train/test)
  • 660 Number of Attributes

Response Surface

Polynomial Kernel

  • mnist Data Set Name
  • 10 Number of Classes
  • 56000/14000 Number of Instances (train/test)
  • 660 Number of Attributes

Response Surface

RBF-Kernel

  • mnist Data Set Name
  • 10 Number of Classes
  • 56000/14000 Number of Instances (train/test)
  • 660 Number of Attributes

Response Surface

Linear Kernel

  • molecular-biology Data Set Name
  • 4 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

Polynomial Kernel

  • molecular-biology Data Set Name
  • 4 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

RBF-Kernel

  • molecular-biology Data Set Name
  • 4 Number of Classes
  • 84/22 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

Linear Kernel

  • monk-2 Data Set Name
  • 2 Number of Classes
  • 345/87 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Polynomial Kernel

  • monk-2 Data Set Name
  • 2 Number of Classes
  • 345/87 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

RBF-Kernel

  • monk-2 Data Set Name
  • 2 Number of Classes
  • 345/87 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Linear Kernel

  • morphological Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Polynomial Kernel

  • morphological Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

RBF-Kernel

  • morphological Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 6 Number of Attributes

Response Surface

Linear Kernel

  • movement_libras Data Set Name
  • 15 Number of Classes
  • 288/72 Number of Instances (train/test)
  • 90 Number of Attributes

Response Surface

Polynomial Kernel

  • movement_libras Data Set Name
  • 15 Number of Classes
  • 288/72 Number of Instances (train/test)
  • 90 Number of Attributes

Response Surface

RBF-Kernel

  • movement_libras Data Set Name
  • 15 Number of Classes
  • 288/72 Number of Instances (train/test)
  • 90 Number of Attributes

Response Surface

Linear Kernel

  • mushrooms Data Set Name
  • 2 Number of Classes
  • 6499/1625 Number of Instances (train/test)
  • 107 Number of Attributes

Response Surface

Polynomial Kernel

  • mushrooms Data Set Name
  • 2 Number of Classes
  • 6499/1625 Number of Instances (train/test)
  • 107 Number of Attributes

Response Surface

RBF-Kernel

  • mushrooms Data Set Name
  • 2 Number of Classes
  • 6499/1625 Number of Instances (train/test)
  • 107 Number of Attributes

Response Surface

Linear Kernel

  • news20m Data Set Name
  • 20 Number of Classes
  • 12748/3187 Number of Instances (train/test)
  • 28520 Number of Attributes

Response Surface

Polynomial Kernel

  • news20m Data Set Name
  • 20 Number of Classes
  • 12748/3187 Number of Instances (train/test)
  • 28520 Number of Attributes

Response Surface

RBF-Kernel

  • news20m Data Set Name
  • 20 Number of Classes
  • 12748/3187 Number of Instances (train/test)
  • 28520 Number of Attributes

Response Surface

Linear Kernel

  • newthyroid Data Set Name
  • 3 Number of Classes
  • 172/43 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Polynomial Kernel

  • newthyroid Data Set Name
  • 3 Number of Classes
  • 172/43 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

RBF-Kernel

  • newthyroid Data Set Name
  • 3 Number of Classes
  • 172/43 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Linear Kernel

  • nursery Data Set Name
  • 5 Number of Classes
  • 10368/2592 Number of Instances (train/test)
  • 27 Number of Attributes

Response Surface

Polynomial Kernel

  • nursery Data Set Name
  • 5 Number of Classes
  • 10368/2592 Number of Instances (train/test)
  • 27 Number of Attributes

Response Surface

RBF-Kernel

  • nursery Data Set Name
  • 5 Number of Classes
  • 10368/2592 Number of Instances (train/test)
  • 27 Number of Attributes

Response Surface

Linear Kernel

  • optdigits Data Set Name
  • 10 Number of Classes
  • 4496/1124 Number of Instances (train/test)
  • 61 Number of Attributes

Response Surface

Polynomial Kernel

  • optdigits Data Set Name
  • 10 Number of Classes
  • 4496/1124 Number of Instances (train/test)
  • 61 Number of Attributes

Response Surface

RBF-Kernel

  • optdigits Data Set Name
  • 10 Number of Classes
  • 4496/1124 Number of Instances (train/test)
  • 61 Number of Attributes

Response Surface

Linear Kernel

  • page-blocks Data Set Name
  • 5 Number of Classes
  • 4377/1095 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • page-blocks Data Set Name
  • 5 Number of Classes
  • 4377/1095 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • page-blocks Data Set Name
  • 5 Number of Classes
  • 4377/1095 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • pendigits Data Set Name
  • 10 Number of Classes
  • 8793/2199 Number of Instances (train/test)
  • 15 Number of Attributes

Response Surface

Polynomial Kernel

  • pendigits Data Set Name
  • 10 Number of Classes
  • 8793/2199 Number of Instances (train/test)
  • 15 Number of Attributes

Response Surface

RBF-Kernel

  • pendigits Data Set Name
  • 10 Number of Classes
  • 8793/2199 Number of Instances (train/test)
  • 15 Number of Attributes

Response Surface

Linear Kernel

  • phoneme Data Set Name
  • 2 Number of Classes
  • 4323/1081 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Polynomial Kernel

  • phoneme Data Set Name
  • 2 Number of Classes
  • 4323/1081 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

RBF-Kernel

  • phoneme Data Set Name
  • 2 Number of Classes
  • 4323/1081 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Linear Kernel

  • pima Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • pima Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • pima Data Set Name
  • 2 Number of Classes
  • 614/154 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • pixel Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 239 Number of Attributes

Response Surface

Polynomial Kernel

  • pixel Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 239 Number of Attributes

Response Surface

RBF-Kernel

  • pixel Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 239 Number of Attributes

Response Surface

Linear Kernel

  • post-operative Data Set Name
  • 3 Number of Classes
  • 69/18 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

Polynomial Kernel

  • post-operative Data Set Name
  • 3 Number of Classes
  • 69/18 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

RBF-Kernel

  • post-operative Data Set Name
  • 3 Number of Classes
  • 69/18 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

Linear Kernel

  • rcv1 Data Set Name
  • 2 Number of Classes
  • 16193/4049 Number of Instances (train/test)
  • 46195 Number of Attributes

Response Surface

Polynomial Kernel

  • rcv1 Data Set Name
  • 2 Number of Classes
  • 16193/4049 Number of Instances (train/test)
  • 46195 Number of Attributes

Response Surface

RBF-Kernel

  • rcv1 Data Set Name
  • 2 Number of Classes
  • 16193/4049 Number of Instances (train/test)
  • 46195 Number of Attributes

Response Surface

Linear Kernel

  • real-sim Data Set Name
  • 2 Number of Classes
  • 57847/14462 Number of Instances (train/test)
  • 20508 Number of Attributes

Response Surface

Polynomial Kernel

  • real-sim Data Set Name
  • 2 Number of Classes
  • 57847/14462 Number of Instances (train/test)
  • 20508 Number of Attributes

Response Surface

RBF-Kernel

  • real-sim Data Set Name
  • 2 Number of Classes
  • 57847/14462 Number of Instances (train/test)
  • 20508 Number of Attributes

Response Surface

Linear Kernel

  • ring Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • ring Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • ring Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • saheart Data Set Name
  • 2 Number of Classes
  • 369/93 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • saheart Data Set Name
  • 2 Number of Classes
  • 369/93 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • saheart Data Set Name
  • 2 Number of Classes
  • 369/93 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • satimage Data Set Name
  • 6 Number of Classes
  • 3548/887 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

Polynomial Kernel

  • satimage Data Set Name
  • 6 Number of Classes
  • 3548/887 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

RBF-Kernel

  • satimage Data Set Name
  • 6 Number of Classes
  • 3548/887 Number of Instances (train/test)
  • 36 Number of Attributes

Response Surface

Linear Kernel

  • segment Data Set Name
  • 7 Number of Classes
  • 1848/462 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Polynomial Kernel

  • segment Data Set Name
  • 7 Number of Classes
  • 1848/462 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

RBF-Kernel

  • segment Data Set Name
  • 7 Number of Classes
  • 1848/462 Number of Instances (train/test)
  • 19 Number of Attributes

Response Surface

Linear Kernel

  • seismic Data Set Name
  • 3 Number of Classes
  • 78822/19706 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

Polynomial Kernel

  • seismic Data Set Name
  • 3 Number of Classes
  • 78822/19706 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

RBF-Kernel

  • seismic Data Set Name
  • 3 Number of Classes
  • 78822/19706 Number of Instances (train/test)
  • 50 Number of Attributes

Response Surface

Linear Kernel

  • shuttle Data Set Name
  • 7 Number of Classes
  • 34800/8700 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Polynomial Kernel

  • shuttle Data Set Name
  • 7 Number of Classes
  • 34800/8700 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

RBF-Kernel

  • shuttle Data Set Name
  • 7 Number of Classes
  • 34800/8700 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Linear Kernel

  • solar-flare Data Set Name
  • 3 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • solar-flare Data Set Name
  • 3 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • solar-flare Data Set Name
  • 3 Number of Classes
  • 852/214 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • sonar-scale Data Set Name
  • 2 Number of Classes
  • 166/42 Number of Instances (train/test)
  • 60 Number of Attributes

Response Surface

Polynomial Kernel

  • sonar-scale Data Set Name
  • 2 Number of Classes
  • 166/42 Number of Instances (train/test)
  • 60 Number of Attributes

Response Surface

RBF-Kernel

  • sonar-scale Data Set Name
  • 2 Number of Classes
  • 166/42 Number of Instances (train/test)
  • 60 Number of Attributes

Response Surface

Linear Kernel

  • spambase Data Set Name
  • 2 Number of Classes
  • 3677/920 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

Polynomial Kernel

  • spambase Data Set Name
  • 2 Number of Classes
  • 3677/920 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

RBF-Kernel

  • spambase Data Set Name
  • 2 Number of Classes
  • 3677/920 Number of Instances (train/test)
  • 57 Number of Attributes

Response Surface

Linear Kernel

  • spectfheart Data Set Name
  • 2 Number of Classes
  • 213/54 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

Polynomial Kernel

  • spectfheart Data Set Name
  • 2 Number of Classes
  • 213/54 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

RBF-Kernel

  • spectfheart Data Set Name
  • 2 Number of Classes
  • 213/54 Number of Instances (train/test)
  • 44 Number of Attributes

Response Surface

Linear Kernel

  • splice Data Set Name
  • 2 Number of Classes
  • 2540/635 Number of Instances (train/test)
  • 58 Number of Attributes

Response Surface

Polynomial Kernel

  • splice Data Set Name
  • 2 Number of Classes
  • 2540/635 Number of Instances (train/test)
  • 58 Number of Attributes

Response Surface

RBF-Kernel

  • splice Data Set Name
  • 2 Number of Classes
  • 2540/635 Number of Instances (train/test)
  • 58 Number of Attributes

Response Surface

Linear Kernel

  • svmguide1 Data Set Name
  • 2 Number of Classes
  • 5671/1418 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Polynomial Kernel

  • svmguide1 Data Set Name
  • 2 Number of Classes
  • 5671/1418 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

RBF-Kernel

  • svmguide1 Data Set Name
  • 2 Number of Classes
  • 5671/1418 Number of Instances (train/test)
  • 4 Number of Attributes

Response Surface

Linear Kernel

  • svmguide2 Data Set Name
  • 3 Number of Classes
  • 312/79 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • svmguide2 Data Set Name
  • 3 Number of Classes
  • 312/79 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • svmguide2 Data Set Name
  • 3 Number of Classes
  • 312/79 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • svmguide3 Data Set Name
  • 2 Number of Classes
  • 1027/257 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • svmguide3 Data Set Name
  • 2 Number of Classes
  • 1027/257 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • svmguide3 Data Set Name
  • 2 Number of Classes
  • 1027/257 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • svmguide4 Data Set Name
  • 6 Number of Classes
  • 489/123 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • svmguide4 Data Set Name
  • 6 Number of Classes
  • 489/123 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • svmguide4 Data Set Name
  • 6 Number of Classes
  • 489/123 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • tae Data Set Name
  • 3 Number of Classes
  • 120/31 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Polynomial Kernel

  • tae Data Set Name
  • 3 Number of Classes
  • 120/31 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

RBF-Kernel

  • tae Data Set Name
  • 3 Number of Classes
  • 120/31 Number of Instances (train/test)
  • 5 Number of Attributes

Response Surface

Linear Kernel

  • texture Data Set Name
  • 11 Number of Classes
  • 4400/1100 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Polynomial Kernel

  • texture Data Set Name
  • 11 Number of Classes
  • 4400/1100 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

RBF-Kernel

  • texture Data Set Name
  • 11 Number of Classes
  • 4400/1100 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Linear Kernel

  • thyroid Data Set Name
  • 3 Number of Classes
  • 5760/1440 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

Polynomial Kernel

  • thyroid Data Set Name
  • 3 Number of Classes
  • 5760/1440 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

RBF-Kernel

  • thyroid Data Set Name
  • 3 Number of Classes
  • 5760/1440 Number of Instances (train/test)
  • 21 Number of Attributes

Response Surface

Linear Kernel

  • tic-tac-toe Data Set Name
  • 2 Number of Classes
  • 766/192 Number of Instances (train/test)
  • 26 Number of Attributes

Response Surface

Polynomial Kernel

  • tic-tac-toe Data Set Name
  • 2 Number of Classes
  • 766/192 Number of Instances (train/test)
  • 26 Number of Attributes

Response Surface

RBF-Kernel

  • tic-tac-toe Data Set Name
  • 2 Number of Classes
  • 766/192 Number of Instances (train/test)
  • 26 Number of Attributes

Response Surface

Linear Kernel

  • titanic Data Set Name
  • 2 Number of Classes
  • 1760/441 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Polynomial Kernel

  • titanic Data Set Name
  • 2 Number of Classes
  • 1760/441 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

RBF-Kernel

  • titanic Data Set Name
  • 2 Number of Classes
  • 1760/441 Number of Instances (train/test)
  • 3 Number of Attributes

Response Surface

Linear Kernel

  • twonorm Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Polynomial Kernel

  • twonorm Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

RBF-Kernel

  • twonorm Data Set Name
  • 2 Number of Classes
  • 5920/1480 Number of Instances (train/test)
  • 20 Number of Attributes

Response Surface

Linear Kernel

  • usps Data Set Name
  • 10 Number of Classes
  • 7438/1860 Number of Instances (train/test)
  • 255 Number of Attributes

Response Surface

Polynomial Kernel

  • usps Data Set Name
  • 10 Number of Classes
  • 7438/1860 Number of Instances (train/test)
  • 255 Number of Attributes

Response Surface

RBF-Kernel

  • usps Data Set Name
  • 10 Number of Classes
  • 7438/1860 Number of Instances (train/test)
  • 255 Number of Attributes

Response Surface

Linear Kernel

  • vehicle Data Set Name
  • 4 Number of Classes
  • 676/170 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

Polynomial Kernel

  • vehicle Data Set Name
  • 4 Number of Classes
  • 676/170 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

RBF-Kernel

  • vehicle Data Set Name
  • 4 Number of Classes
  • 676/170 Number of Instances (train/test)
  • 18 Number of Attributes

Response Surface

Linear Kernel

  • vowel Data Set Name
  • 11 Number of Classes
  • 422/106 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Polynomial Kernel

  • vowel Data Set Name
  • 11 Number of Classes
  • 422/106 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

RBF-Kernel

  • vowel Data Set Name
  • 11 Number of Classes
  • 422/106 Number of Instances (train/test)
  • 10 Number of Attributes

Response Surface

Linear Kernel

  • W8A Data Set Name
  • 2 Number of Classes
  • 51760/12940 Number of Instances (train/test)
  • 299 Number of Attributes

Response Surface

Polynomial Kernel

  • W8A Data Set Name
  • 2 Number of Classes
  • 51760/12940 Number of Instances (train/test)
  • 299 Number of Attributes

Response Surface

RBF-Kernel

  • W8A Data Set Name
  • 2 Number of Classes
  • 51760/12940 Number of Instances (train/test)
  • 299 Number of Attributes

Response Surface

Linear Kernel

  • waveform-5000 Data Set Name
  • 3 Number of Classes
  • 4000/1000 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Polynomial Kernel

  • waveform-5000 Data Set Name
  • 3 Number of Classes
  • 4000/1000 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

RBF-Kernel

  • waveform-5000 Data Set Name
  • 3 Number of Classes
  • 4000/1000 Number of Instances (train/test)
  • 40 Number of Attributes

Response Surface

Linear Kernel

  • wdbc Data Set Name
  • 2 Number of Classes
  • 455/114 Number of Instances (train/test)
  • 30 Number of Attributes

Response Surface

Polynomial Kernel

  • wdbc Data Set Name
  • 2 Number of Classes
  • 455/114 Number of Instances (train/test)
  • 30 Number of Attributes

Response Surface

RBF-Kernel

  • wdbc Data Set Name
  • 2 Number of Classes
  • 455/114 Number of Instances (train/test)
  • 30 Number of Attributes

Response Surface

Linear Kernel

  • wine Data Set Name
  • 3 Number of Classes
  • 142/36 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Polynomial Kernel

  • wine Data Set Name
  • 3 Number of Classes
  • 142/36 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

RBF-Kernel

  • wine Data Set Name
  • 3 Number of Classes
  • 142/36 Number of Instances (train/test)
  • 13 Number of Attributes

Response Surface

Linear Kernel

  • winequality-red Data Set Name
  • 6 Number of Classes
  • 1279/320 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

Polynomial Kernel

  • winequality-red Data Set Name
  • 6 Number of Classes
  • 1279/320 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

RBF-Kernel

  • winequality-red Data Set Name
  • 6 Number of Classes
  • 1279/320 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

Linear Kernel

  • winequality-white Data Set Name
  • 7 Number of Classes
  • 3918/980 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

Polynomial Kernel

  • winequality-white Data Set Name
  • 7 Number of Classes
  • 3918/980 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

RBF-Kernel

  • winequality-white Data Set Name
  • 7 Number of Classes
  • 3918/980 Number of Instances (train/test)
  • 11 Number of Attributes

Response Surface

Linear Kernel

  • wisconsin Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Polynomial Kernel

  • wisconsin Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

RBF-Kernel

  • wisconsin Data Set Name
  • 2 Number of Classes
  • 546/137 Number of Instances (train/test)
  • 9 Number of Attributes

Response Surface

Linear Kernel

  • yeast Data Set Name
  • 10 Number of Classes
  • 1187/297 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Polynomial Kernel

  • yeast Data Set Name
  • 10 Number of Classes
  • 1187/297 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

RBF-Kernel

  • yeast Data Set Name
  • 10 Number of Classes
  • 1187/297 Number of Instances (train/test)
  • 8 Number of Attributes

Response Surface

Linear Kernel

  • zernike Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 47 Number of Attributes

Response Surface

Polynomial Kernel

  • zernike Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 47 Number of Attributes

Response Surface

RBF-Kernel

  • zernike Data Set Name
  • 10 Number of Classes
  • 1600/400 Number of Instances (train/test)
  • 47 Number of Attributes

Response Surface

Linear Kernel

  • zoo Data Set Name
  • 7 Number of Classes
  • 80/21 Number of Instances (train/test)
  • 35 Number of Attributes

Response Surface

Polynomial Kernel

  • zoo Data Set Name
  • 7 Number of Classes
  • 80/21 Number of Instances (train/test)
  • 35 Number of Attributes

Response Surface

RBF-Kernel

  • zoo Data Set Name
  • 7 Number of Classes
  • 80/21 Number of Instances (train/test)
  • 35 Number of Attributes