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Mitchell T.M. Ч Machine Learning
Mitchell T.M. Ч Machine Learning

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Ќазвание: Machine Learning

јвтор: Mitchell T.M.


This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning

язык: en

–убрика: “ехнологи€/

—татус предметного указател€: √отов указатель с номерами страниц

ed2k: ed2k stats

√од издани€: 1997

 оличество страниц: 432

ƒобавлена в каталог: 21.06.2006

ќперации: ѕоложить на полку | —копировать ссылку дл€ форума | —копировать ID
ѕредметный указатель
$TD(\lambda)$      383Ч384 387
$\theta$-subsumption      302
$\theta$-subsumption, relationship with entailment and more_general_than partial ordering      299Ч300
Absorbing state      371
ABSTRIPS      329
Acyclic neural networks      See "Multilayer feedforward networks"
Adaline rule      See "Delta rule"
Additive Chernoff bounds      210Ч211
Adelines      123
Agents in reinforcement learning      368
Agnostic learning      210Ч211 225
ALVINN system      82Ч84
Analytical learning      307Ч330
Analytical learning, inductive learning, comparison with      310 328Ч329 334Ч336 362
Analytical-inductive learning      See "Inductive-analytical learning"
ANN learning      See "Neural network learning"
ANNs      See "Neural networks artificial"
Antecedents of Horn clause      285
AQ algorithm      279Ч280
AQ14 algorithm, comparison with GABIL      256 258
Arbitrary functions, representation by feedforward networks      105Ч106
Artificial intelligence, influence on machine learning      4
Artificial Neural Networks      See "Neural networks artificial"
Assistant      77
Astronomical structures, machine learning classification of      3
Attributes, choice of, in sequential vs. simultaneous covering algorithms      280Ч281
Attributes, continuous-valued      72Ч73
Attributes, cost-sensitive measures      75Ч76
Attributes, discrete-valued      72
Attributes, measures for selection of      73Ч74 77
Attributes, missing values, strategies for      75
Autonomous vehicles      3 4 82Ч84
Average reward      371
Backgammon learning program      See "TD-Gammon"
Backpropagation algorithm      83 97 124
Backpropagation algorithm in Q learning      384
Backpropagation algorithm, applications of      81 84 85 96 113
Backpropagation algorithm, convergence and local minima      104Ч105
Backpropagation algorithm, definition of      98
Backpropagation algorithm, discovery of hidden layer representations      106Ч109 123
Backpropagation algorithm, feedforward networks as hypothesis space      105Ч106
Backpropagation algorithm, gradient descent search      89 115Ч116 123
Backpropagation algorithm, inductive bias of      106
Backpropagation algorithm, KBANN algorithm, comparison with      344Ч345
Backpropagation algorithm, KBANN algorithm, use in      339
Backpropagation algorithm, momentum, addition of      100 104
Backpropagation algorithm, overfitting in      108 110Ч111
Backpropagation algorithm, search of hypothesis space      97 106 122Ч123
Backpropagation algorithm, search of hypothesis space by genetic algorithms, comparison with      259
Backpropagation algorithm, search of hypothesis space by KBANN and TangentProp algorithms, comparison with      350Ч351
Backpropagation algorithm, search of hypothesis space in decision tree learning, comparison with      106
Backpropagation algorithm, stochastic gradient descent version      98Ч100 104Ч105 107Ч108
Backpropagation algorithm, TangentProp algorithm, comparison with      349
Backpropagation algorithm, weight update rule for hidden unit weights      103
Backpropagation algorithm, weight update rule for output unit weights      102Ч103 171
Backpropagation algorithm, weight update rule in KBANN algorithm      343Ч344
Backpropagation algorithm, weight update rule, alternative error functions      117Ч118
Backpropagation algorithm, weight update rule, derivation of      101Ч102
Backpropagation algorithm, weight update rule, optimization methods      119
Backtracking, ID3 algorithm and      62
Backward chaining search for explanation generation      314
Baldwin effect      250 267
Baldwin effect, computational models for      267Ч268
Bayes classifier, naive      See "Naive Bayes classifier"
Bayes optimal classifier      174Ч176 197 222
Bayes optimal classifier, learning Boolean concepts using version spaces      176
Bayes optimal learner      See "Bayes optimal classifier"
Bayes rule      See "Bayes theorem"
Bayes theorem      4 156Ч159
Bayes theorem in Brute-Force MAP Learning algorithm      160Ч162
Bayes theorem in inductive-analytical learning      338
Bayes theorem, concept learning and      158Ч163
Bayesian belief networks      184Ч191
Bayesian belief networks, choice among alternative networks      190
Bayesian belief networks, conditional independence in      185
Bayesian belief networks, constraint-based approaches in      191
Bayesian belief networks, gradient ascent search in      188Ч190
Bayesian belief networks, inference methods      187Ч188
Bayesian belief networks, joint probability distribution representation      185Ч187
Bayesian belief networks, learning from training data      188Ч191
Bayesian belief networks, naive Bayes classifier, comparison with      186
Bayesian belief networks, representation of causal knowledge      187
Bayesian classifiers      198 See "Naive
Bayesian learning      154Ч198
Bayesian learning, decision tree learning, comparison with      198
Bayesian methods, influence on machine learning      4
Beam search, general-to-specific      See "General-to-specific beam search"
Beam search, generate-and-test      See "Generate-and-test beam search"
Bellman residual errors      385
Bellman Ч Ford shortest path algorithm      386 387
Bellman's equation      385Ч386
BFS-ID3 algorithm      63
Binomial distribution      133Ч137 143 151
Biological evolution      249 250 266Ч267
Biological neural networks, comparison with artificial neural networks      82
Bit strings      252Ч253 258Ч259 269
Blocks, stacking of      See "Stacking problems"
Body of Horn clause      285
Boolean conjunctions, PAC learning of      211Ч212
Boolean functions, representation by feedforward networks      105Ч106
Boolean functions, representation by perceptrons      87Ч88
Boundary set representation for version spaces      31Ч36
Boundary set representation for version spaces, definition of      31
Bounds, one-sided      141 144
Bounds, two-sided      141
Brain, neural activity in      82
Breadth first search in ID3 algorithm      63
Brute-Force MAP Learning algorithm      159Ч162
Brute-Force MAP Learning algorithm, Bayes theorem in      160Ч162
C4.5 algorithm      55 77
C4.5 algorithm, GABIL, comparison with      256 258
C4.5 algorithm, missing attribute values, method for handling      75
C4.5 algorithm, rule post-pruning in      71Ч72
CADET system      241Ч244
Candidate specializations, generated by FOCL algorithm      357Ч361
Candidate specializations, generated by FOIL algorithm      287Ч288 357Ч358
Candidate-Elimination algorithm      29Ч37 45Ч47
Candidate-Elimination algorithm, applications of      29 302
Candidate-Elimination algorithm, Bayesian interpretation of      163
Candidate-Elimination algorithm, computation of version spaces      32Ч36
Candidate-Elimination algorithm, computation of version spaces, definition of      33
Candidate-Elimination algorithm, ID3 algorithm, comparison with      61Ч64
Candidate-Elimination algorithm, inductive bias of      43Ч46 63Ч64
Candidate-Elimination algorithm, limitations of      29 37 41 42 46
Candidate-Elimination algorithm, search of hypothesis space      64
CART system      77
Cascade-Correlation algorithm      121Ч123
Case-based reasoning      231 240Ч244 246 247
Case-based reasoning, advantages of      243Ч244
Case-based reasoning, applications of      240
Case-based reasoning, other instance-based learning methods, comparison with      240
Causal knowledge, representation by Bayesian belief networks      187
Central limit theorem      133 142Ч143 167
Checkers learning program      2Ч3 5Ч14 387
Checkers learning program as sequential control process      369
Checkers learning program, algorithms for      14
Checkers learning program, design      13
Chemical mass spectroscopy, Candidate-Elimination algorithm in      29
Chess learning program      308Ч310
Chess learning program, explanation-based learning in      325
chunking      327 330
Cigol      302
Circuit design, genetic programming in      265Ч266
Circuit layout, genetic algorithms in      256
Classification problems      54
Classify_naive_Bayes_text      182Ч183
CLAUDIEN      302
clauses      284 285
CLS      See "Concept Learning System"
Clustering      191
CN2 algorithm      278 301
CN2 algorithm, choice of attribute-pairs in      280Ч281
Complexity, sample      See "Sample complexity"
Computational complexity      202
Computational complexity theory, influence on machine learning      4
Computational learning theory      201Ч227
Concept learning      20Ч47
Concept Learning System      77
Concept learning, algorithms for      47
Concept learning, Bayes theorem and      158Ч163
Concept learning, definition of      21
Concept learning, genetic algorithms in      256
Concept learning, ID3 algorithm specialized for      56
Concept learning, notation for      22Ч23
Concept learning, search of hypothesis space      23Ч25 46Ч47
Concept learning, task design in      21Ч22
Concepts, partially learned      38Ч39
Conditional independence      185
Conditional independence in Bayesian belief networks      186Ч187
confidence intervals      133 138Ч141 150 151
Confidence intervals for discrete-valued hypotheses      131Ч132 140Ч141
Confidence intervals for discrete-valued hypotheses, derivation of      142Ч143
Confidence intervals, one-sided      144 145
Conjugate gradient method      119
Conjunction of boolean literals, PAC learning of      211Ч212
Consequent of Horn clause      285
Consistent learners      162Ч163
Consistent learners, bound on sample complexity      207Ч210 225
Consistent learners, bound on sample complexity, equation for      209
Constants in logic      284 285
Constraint-based approaches in Bayesian belief networks      191
Constructive induction      292
Continuous functions, representation by feedforward networks      105Ч106
Continuous-valued hypotheses, training error of      89Ч90
Continuous-valued target function      197
Continuous-valued target function, maximum likelihood (ML) hypothesis for      164Ч167
Control theory, influence on machine learning      4
Convergence of Q learning algorithm in deterministic environments      377Ч380 386
Convergence of Q learning algorithm in nondeterministic environments      382Ч383 386
Credit assignment      5
Critic      12 13
Cross entropy      170
Cross entropy, minimization of      118
Cross-validation      111Ч112
Cross-validation for comparison of learning algorithms      145Ч151
Cross-validation in k-Nearest Neighbor algorithm      235
Cross-validation in neural network learning      111Ч112
Cross-validation, k-fold      See "k-fold cross-validation"
Cross-validation, leave-one-out      235
Crossover mask      254
Crossover operators      252Ч254 261 262
Crossover operators, single-point      254 261
Crossover operators, two-point      254 257Ч258
Crossover operators, uniform      255
Crowding      259
Cumulative reward      371
Curse of dimensionality      235
Data Mining      17
Decision tree learning      52Ч77
Decision tree learning, algorithms for      55 77 See "ID3
Decision tree learning, applications of      54
Decision tree learning, Bayesian learning, comparison with      198
Decision tree learning, impact of pruning on accuracy      128Ч129
Decision tree learning, inductive bias in      63Ч66
Decision tree learning, k-Nearest Neighbor algorithm, comparison with      235
Decision tree learning, Minimum Description Length principle in      173Ч174
Decision tree learning, neural network learning, comparison with      85
Decision tree learning, overfitting in      67Ч69 76Ч77 111
Decision tree learning, post-pruning in      68Ч69 77
Decision tree learning, reduced-error pruning in      69Ч71
Decision tree learning, rule post-pruning in      71Ч72 281
Decision tree learning, search of hypothesis space      60Ч62
Decision tree learning, search of hypothesis space by Backpropagation algorithm, comparison with      106
Deductive learning      321Ч322
Degrees of freedom      147
Delayed learning methods, comparison with eager learning      244Ч245
Delayed reward in reinforcement learning      369
Delta rule      11 88Ч90 94 99 123
Demes      268
Determinations      325
Deterministic environments, Q learning algorithm for      375
Directed acyclic neural networks      See "Multilayer feedforward networks"
Discounted cumulative reward      371
Discrete-valued hypotheses, confidence intervals for      131Ч132 140Ч141
Discrete-valued hypotheses, confidence intervals for, derivation of      142Ч143
Discrete-valued hypotheses, training error of      205
Discrete-valued target functions, approximation by decision tree learning      52
Disjunctive sets of rules, learning by sequential covering algorithms      275Ч276
Distance-weighted k-Nearest Neighbor algorithm      233Ч234
Domain theory      310 329 See "Perfect "Prior
Domain theory as KBANN neural network      342Ч343
Domain theory in analytical learning      311Ч312
Domain theory in Prolog-EBG      322
Domain theory, weighting of components in EBNN      351Ч352
Domain-independent learning algorithms      336
Dyna      380
Dynamic programming, applications to reinforcement learning      380
Dynamic programming, reinforcement learning and      385Ч387
Eager learning methods, comparison with lazy learning      244Ч245
EBG algorithm      313
EBNN algorithm      351Ч356 362 387
EBNN algorithm, other explanation-based learning methods, comparison with      356
EBNN algorithm, prior knowledge and gradient descent in      339
EBNN algorithm, TangentProp algorithm in      353
EBNN algorithm, weighting of inductive-analytical components in      355 362
EGGS algorithm      313
EM algorithm      190Ч196 197
EM algorithm, applications of      191 194
EM algorithm, derivation of algorithm for k-means      195Ч196
EM algorithm, search for maximum likelihood (ML) hypothesis      194Ч195
Entailment      321n
Entailment, relationship with $\theta$-subsumption and more_general_than partial ordering      299Ч300
entropy      55Ч57 282
Entropy of optimal code      172n
Environment in reinforcement learning      368
Equivalent sample size      179Ч180
Error bars for discrete-valued hypotheses      See "Confidence intervals for discrete-valued hypotheses"
Error of hypotheses, sample      See "Sample error"
Error of hypotheses, training      See "Training error"
Error of hypotheses, true      See "True error"
Estimation bias      133 137Ч138 151
Estimator      133 137Ч138 143 150Ч151
Evolution of populations in genetic algorithms      260Ч262
Evolution of populations, argument for Occam's razor      66
Evolutionary computation      250 262
Evolutionary computation, applications of      269
Example-driven search, comparison with generate-and-test beam search      281
Expected value      133 136
Experiment generator      12Ч13
Explanation-based learning      312Ч330
Explanation-based learning, applications of      325Ч328
Explanation-based learning, derivation of new features      320Ч321
Explanation-based learning, inductive bias in      322Ч323
Explanation-based learning, inductive learning and      330
Explanation-based learning, lazy methods in      328
Explanation-based learning, limitations of      308 329
Explanation-based learning, prior knowledge in      308Ч309
Explanation-based learning, reinforcement learning and      330
Explanation-based learning, utility analysis in      327Ч328
Explanations generated by backward chaining search      314
Explicit prior knowledge      329
Exploration in reinforcement learning      369
Face recognition      17
Face recognition, Backpropagation algorithm in      81 112Ч117
Feedforward networks      See "Multilayer feedforward networks"
Find-S algorithm      26Ч28 46
Find-S algorithm, Bayesian interpretation of      162Ч163
Find-S algorithm, definition of      26
Find-S algorithm, inductive bias of      45
Find-S algorithm, limitations of      28Ч29
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