[83] | 1 | % PRDATASETS: Pattern Recognition Datasets in PRTools format |
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[141] | 2 | % Version 3.0 22-Dec-2019 |
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[80] | 3 | % |
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[83] | 4 | %Feature based labeled datasets |
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| 5 | %------------------------------ |
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| 6 | %name objects feats classes |
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| 7 | %x80 45 8 3 radial distances of characters |
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| 8 | %arrhythmia 420 278 2 presence or absence of cardia arrhythmia |
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| 9 | %auto_mpg 398 6 2 car/miles-per-gallon |
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| 10 | %biomed 194 5 2 various patient indicators |
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| 11 | %breast 683 9 2 Wisconsion breast cancer dataset |
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[140] | 12 | %car 1728 6 4 Car evaluation database |
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[83] | 13 | %cbands 12000 30 24 chromosome banding patterns |
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| 14 | %chromo 1143 8 24 chromosome blob features |
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| 15 | %diabetes 768 8 2 Pima Indians Diabetes Database |
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| 16 | %ecoli 272 7 3 protein localisation sites |
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| 17 | %glass 214 9 4 glass types from chemical components |
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| 18 | %heart 297 13 2 heart disease dataset |
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[140] | 19 | %hepatitis 112 19 2 hepatitis database |
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[83] | 20 | %imox 192 8 4 radial distances of characters |
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| 21 | %ionosphere 351 34 2 radar data |
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| 22 | %iris 150 4 3 Fisher's Iris dataset |
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| 23 | %liver 345 6 2 liver disorder |
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[140] | 24 | %malaysia 291 8 20 segment features in utility symbols |
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[83] | 25 | %satellite 6435 36 6 spectral data |
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| 26 | %sonar 208 60 2 rock / metal sonar features |
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| 27 | %soybean1 266 35 19 large Soybeans |
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| 28 | %soybean2 136 35 4 small Soybeans |
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| 29 | %twonorm 7400 20 2 Leo Breiman's two normal example. |
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| 30 | %ringnorm 7400 20 2 Leo Breiman's ringnorm example. |
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| 31 | %wine 178 13 3 wine recognition |
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| 32 | %mfeat_fac 2000 216 10 face features in digits dataset |
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| 33 | %mfeat_fou 2000 76 10 Fourier features in digits dataset |
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| 34 | %mfeat_kar 2000 64 10 Karhunen Loeve features in digits dataset |
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| 35 | %mfeat_pix 2000 240 10 pixel features in digits dataset |
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| 36 | %mfeat_zer 2000 53 10 Zernike moments in digits dataset |
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| 37 | %mfeat_mor 2000 6 10 morphological features in digits dataset |
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[140] | 38 | %mfeat 2000 649 10 combined features of the mfeat datasets |
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[80] | 39 | % |
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[83] | 40 | %Multi-band images (pixels are objects, bands are features) |
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[80] | 41 | %---------------------------------------------------------- |
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[83] | 42 | %name pixels bands classes |
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| 43 | %emim 128*128 8 1 A seto of 5 8-band EM images |
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| 44 | %lena 256*256 3 1 full-color image |
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| 45 | %texturel 5*128*128 7 5 texture features for 5 different texture images |
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| 46 | %texturet 256*256 7 5 composite texture image |
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[80] | 47 | % |
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[83] | 48 | %Image datasets (pixels are features, images are objects) |
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| 49 | %-------------------------------------------------------- |
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| 50 | %name images pixels classes |
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| 51 | %kimia 216 64*64 18 resampled Kimia dataset of silhouettes |
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[140] | 52 | %mnist8 70000 8*8 10 normalized MNIST digits |
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| 53 | %nist16 2000 16*16 10 normalized NIST digits |
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| 54 | %nist32 5000 32*32 10 resemapled MNIST digits |
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