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| Авторизация |
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| Поиск по указателям |
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| Suykens J.A.K., Horvath G. — Advanced learning theory: methods, moduls and applications |
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| Предметный указатель |
 -projection 328
 -nearest neighbor algorithm 114
-nearest neighbor estimate 343
 -grams 220
 -mixing 368
 -insensitive loss function 389
 -support vector classifiers 181
Absolute cost function 382
Active learning 42
Adaboost 255
Admissible set 72
Algorithmic stability 149
Annealed VC-entropy 9
Approximation 69
Approximation error 32
Automatic relevance determination 163 275
Auxiliary field 309
Average binary loss 258
Average case geometry 331
Backpropagation method 18
Bagging 119
Base learning algorithm 255
Bayesian classification 280
Bayesian decision theory 291
Bayesian field theory 290
Bayesian inference 163 276 322
Bayesian regression 271
Besov spaces 49
Bias-variance problem 41
Black box models 378
Bochner’s theorem 53
Brownian motion 292
Case-based reasoning 320
Centered kernel matrix 165
Closure 71
CMAC 394
Coding matrix 257
Collocation scheme 59
Concept class 360
Conditional distribution function 2
Conditional expectation 344
Conjugate gradient method 136 239
Conjugate prior 326
Consistency 6
Consistent algorithm 365
Convergence in probability 5
Convex 72
Correlation coefficient 166 214
Covariance operator 300
Covering number 33 83 362
Cross-linguistic correlation 212
Cross-model likelihood 334
Cross-validation 112 387
CS-functional 48
Cumulative prediction error 342
Curse of dimensionality 73
Data smoothing 320
Decision trees 256
Deflation 231
Density estimation 3 170 295
Density operator 298
Dependent inputs 367
Diffusion kernel 207
Diffusion process 209
Direct method 136
dispersion 367
Dual variables 135
Eigenfunctions 169
Embedded hardware 394
Empirical density 325
Empirical error 32 113
Empirical mean 358
Empirical risk functional 4
Empirical risk minimization 132 387
Energy 296
Entropy of a set of functions 7
Error correcting output codes 257
Error stability 117
Errors-in-variables 382
Euclidean orthonormal basis 83
Evaluation functional 100
Evaluation space 100
Evaluation subduality 101
Expected risk 132
Exponential family 328
Feature selection 123 243
Filter operator 304
Filtered differences 304
Fisher discriminant analysis 160
Fisher information matrix 330
Fixed-size LS — SVM 170
Fourier orthonormal basis 83
Fourier representation 81
Frobenius inner product 211
Fubini’s Theorem 30
Functional learning 89
Gagliardo diagram 49
Gaussian mixture prior 300
Gaussian prior factors 299
Gaussian process prior 299
Gaussian processes 163
Generalization capability 386
Generalization error 113 361
Generalized cross-validation 121 148
Generalized eigenvalue problem 168 214
Glivenko — Cantelli lemma 358
Globally exponentially stable 372
Gram matrix 201
Grey box models 378
Growth function 9
Hamming decoding 259
Hardware complexity 396
Hilbert isomorphism 31
Hilbert space 30
Hoeffding’s Inequality 34 359
Hyperfield 303
Hyperparameter 303
Hyperparameter optimization 279
Hyperparameters 163
Hyperprior 308
Hypertext documents 215
Hypothesis 360
Hypothesis space 253
Hypothesis stability 114
Image classification 138 142
Image completion 303
Incomplete Cholesky factorization 140
Information geometry 327
Information retrieval 198
Information-based inference 324
Invariances 125
Inverse document frequency 203
Inverse quantum theory 298
Inverse temperature 296
Ivanov regularization 132
Joint density 322
Joint probability distribution 2
Karush — Kuhn — Tucker conditions 20 189
Karush — Kuhn — Tucker system 159
Kernel CCA 166 212
Kernel estimate 344
Kernel FDA 159
Kernel machines 119
Kernel PCA 163
Kernel PLS 168 236
Kernel ridge regression 239
Kernelization 189
kernels 89
Kerridge inaccuracy 295 325
Kolmogorov’s -width 79
| Kullback — Leibler distance 292
Kullback — Leibler divergence 327 329
Lagrangian 20 158 182
Laplacian operator 81
Latent semantic indexing 204
Law of Large Numbers 358
Learning machine 2
Learning rate 361
Least squares estimate 344 381
Least squares support vector machines 136 157 236 239 392
Leave-one-out bound 146
Leave-one-out error 113
Lie group 301
Likelihood energy 296
Likelihood field 291
Likelihood function 323
Linear system 32 121 137 159 239
Local averaging estimates 343
Local learning 320
Local modeling 320
Local models 333
Locally weighted geometry 336
Logistic regression 256
loss 2
Loss-based decoding 259
Low-rank approximation 139 168
Margin 184 253
Markov chain 372
Maximal margin hyperplane 19
Maximum a posteriori approximation 293
Maximum entropy estimate 330
Maximum likelihood 382
Maximum likelihood estimate 330
Measurement 377
Mercer kernel 31 77
Mercer’s condition 23 159 184
Minimal empirical risk algorithm 363
Misclassification error 113
Model complexity 379
Model selection 112 380
Model validation 382
Modeling capability 384
Modulus of continuity 72
Monotonicity 297
Monte Carlo methods 42
Multilayer perceptron 383
Multiple-model prior 333
Natural language processing 199
Newton’s method 38
NIPALS 231
Norm-induced topology 71
Nystr m approximation 139 168
Optimal control 173
Optimal interpolant 63
Outliers 161
Output coding 257
Overfitting 24 42
P-dimension 364
Paley — Wiener theorem 55
Parameter estimation 383
Partial stability 118
Partition sum 296
Partitioning estimate 344
Pattern recognition 3 346
Peetre K-functional 48
Pointwise defined functions 101
Portfolio selection 348
Posterior 291
Posterior density 323
Posterior energy 296
Predictive density 291 324
Primal-dual neural network interpretation 160
Prior 291
Prior information 366 393
Probability measure 30
Probability-based inference 322
Probably approximately correct 360
Proximal support vector machine 135 239
Pruning 161 393
Pythagorean relation 329
Quadratic programming 395
Quadratic Renyi entropy 170
Radial basis function network 383
Random entropy 7
Random VC-entropy 8
Rate of convergence 9
Rayleigh quotient 160
Real normed linear space 71
Real-valued Boolean function 83
Recurrent networks 172
Recursive least squares 170
Reduced form 239
Regression function 3 30 293
Regularization functionals 120
Regularization networks 121 161
Regularization parameter 30 77
Regularized least-squares classification 134
Relevance vector machine 273
Representer theorem 77 91 133
Reproducing kernel 102
Reproducing kernel Hilbert space (RKHS) 31 105 120 132
Ridge regression 120 161
Risk functional 2
Robust statistics 162
Robustness-efficiency trade-off 162
Sample complexity 362
Sample error 32
Semantic proximity matrix 210
Semantic relations 202
Semantic similarity 207
Sensitivity analysis 124
Sherman — Morrison — Woodbury formula 137
Similar-case modeling 332
Similarity measures 200
Singular value decomposition 210
Small sample size 15
Sobolev space 79
Soft margin 158
Sparse models 275
Sparseness 161
Stationary and ergodic process 349
Statistical learning theory 2 358 387
Statistically dependent models 334
Stochastic process 358
String subsequence kernel 217
Structural risk minimization 15 389
Subduality kernel 102
Support vector machines 21 121 133 156 180 254 388
Support vectors 20
Target function 360
Text categorization 138
Tikhonov regularization 133
Total variation metric 366
Transductive inference 123 170
UCI machine learning repository 137 159 240
Underfitting 42
Uniform convergence of empirical means 358
Uniform stability 118
Universal approximators 73
Universally consistent 345
Universally consistent regression estimates 343
Variable-basis approximation 74
Variation w.r.t. set of functions 75
VC dimension 11 364 388
VC entropy 6
VC theory 117
Vector space model 201
Virtual samples 125
von Neumann kernel 209
Vowel-recognizer 70
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