Verfügbarkeit: Machine learning foundations

Machine learning foundations: supervised, unsupervised, and advanced learning

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Bibliographische Detailangaben
1. Verfasser:	Jo, Taeho (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Cham, Switzerland Springer [2021]
Schlagworte:	Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics Electrical engineering Computational intelligence Data mining Information storage and retrieval Big data Maschinelles Lernen
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	xx, 391 Seiten Diagramme
ISBN:	9783030658991

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Datensatz im Suchindex

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adam_text	Contents Part I Foundation 1 Introduction................................................................................................. 1.1 Definition of Machine Learning....................................................... 1.2 Application Areas.............................................................................. 1.2.1 Classification........................................................................ 1.2.2 Regression........................................................................... 1.2.3 Clustering............................................................................ 1.2.4 Hybrid Tasks........................................................................ 1.3 Machine Learning Types................................................................... 1.3.1 Supervised Learning........................................................... 1.3.2 Unsupervised Learning....................................................... 1.3.3 Semi-supervised Learning.................................................. 1.3.4 Reinforcement Learning..................................................... 1.4 Related Areas..................................................................................... 1.4.1 Artificial Intelligence.......................................................... 1.4.2 Neural Networks................................................................. 1.4.3 Data Mining......................................................................... 1.4.4 Soft Computing................................................................... 1.5 Summary and Further Discussions.................................................. References...................................................................................................... 3 3 4 4 6 7 8 11 11 12 14 15 16 17 18 19 20 21 22 2 Numerical Vectors....................................................................................... 2.1 Introduction........................................................................................ 2.2 Operations on Numerical Vectors.................................................... 2.2.1 Definition ............................................................................ 2.2.2 Basic Operations.................................................................. 2.2.3 Inner Product........................................................................ 2.2.4 Linear Independence .......................................................... 2.3 Operations on Matrices..................................................................... 2.3.1 Definition ............................................................................ 2.3.2 Basic Operations................................................................. 23 23 24 24 25 26 28 29 30 31 xiii Contents xiv 2.3.3 Multiplication........................................................................ 2.3.4 Inverse Matrix ...................................................................... Vector and Matrix............................................................................... 2.4.1 Determinant............................................................................ 2.4.2 Eigen Value and Vector........................................................ 2.4.3 Singular Value Decomposition............................................ 2.4.4 Principal Component Analysis............................................ Summary and Further Discussions................................................... 32 35 37 38 40 42 43 45 Data Encoding.............................................................................................. 3.1 Introduction.......................................................................................... 3.2 Relational Data................................................................................... 3.2.1 Basic Concepts...................................................................... 3.2.2 Relational Database............................................................ 3.2.3 Encoding Process................................................................. 3.2.4 Encoding Issues.................................................................... 3.3 Textual Data........................................................................................ 3.3.1 Text Indexing....................................................................... 3.3.2 Text Encoding....................................................................... 3.3.3 Dimension Reduction ......................................................... 3.3.4 Encoding Issues.................................................................... 3.4 Image Data........................................................................................... 3.4.1 Image File Formats.............................................................. 3.4.2 Image Matrix......................................................................... 3.4.3 Encoding Process................................................................. 3.4.4 Encoding Issues.................................................................... 3.5 Summary and Further Discussions................................................... References....................................................................................................... 47 47 48 48 50 52 53 55 55 58 60 61 62 63 63 65 66 67 67 Simple Machine Learning Algorithms.................................................... 4.1 Introduction......................................................................................... 4.2 Classification...................................................................................... 4.2.1 Binary Classification........................................................... 4.2.2 Multiple Classification....................................................... 4.2.3 Regression ........................................................................... 4.2.4 Problem Decomposition...................................................... 4.3 Simple Classifiers.............................................................................. 4.3.1 Threshold Rule..................................................................... 4.3.2 Rectangle.............................................................................. 4.3.3 Hyperplane........................................................................... 4.3.4 Matching Algorithm............................................................ 4.4 Linear Classifiers................................................................................ 4.4.1 Linear Separability ............................................................. 4.4.2 Hyperplane Equation.......................................................... 4.4.3 Linear Classification............................................................ 4.4.4 Perceptron............................................................................. 69 69 70 70 71 73 74 76 76 77 79 80 82 82 84 86 87 2.4 2.5 3 4 Contents XV 4.5 Summary and Further Discussions................................................... References...................................................................................................... Part II 89 90 Supervised Learning 5 Instance Based Learning............................................................................ 5.1 Introduction........................................................................................ 5.2 Primitive Instance Based Learning.................................................. 5.2.1 Look-Up Example.............................................................. 5.2.2 Rule Based Approach........................................................ 5.2.3 Example Similarity............................................................. 5.2.4 One Nearest Neighbor........................................................ 5.3 Classification Process....................................................................... 5.3.1 Notations............................................................................. 5.3.2 Nearest Neighbors.............................................................. 5.3.3 Voting.................................................................................. 5.3.4 Attribute Discriminations.................................................. 5.4 Variants.............................................................................................. 5.4.1 Dynamic Nearest Neighbor............................................... 5.4.2 Concentric Nearest Neighbor............................................ 5.4.3 Hierarchical Nearest Neighbor.......................................... 5.4.4 Hub Examples.................................................................... 5.5 Summary and Further Discussions.................................................. References...................................................................................................... 93 93 94 94 95 98 99 100 100 101 103 105 106 106 108 110 112 113 115 6 Probabilistic Learning............................................................................... 6.1 Introduction........................................................................................ 6.2 Bayes Classifier................................................................................. 6.2.1 Probabilities......................................................................... 6.2.2 Bayes Rule.......................................................................... 6.2.3 Gaussian Distribution ........................................................ 6.2.4 Classification....................................................................... 6.3 Naive Bayes ....................................................................................... 6.3.1 Classification........................................................................ 6.3.2 Learning............................................................................... 6.3.3 Variants................................................................................ 6.3.4 Application to Text Classification..................................... 6.4 Bayesian Learning.............................................................................. 6.4.1 Bayesian Networks............................................................. 6.4.2 Causal Relation................................................................... 6.4.3 Learning Process.................................................................. 6.4.4 Comparisons........................................................................ 6.5 Summary and Further Discussions.................................................. References...................................................................................................... 117 117 118 118 120 121 123 124 124 126 128 129 131 131 133 135 136 138 139 xvi Contents 7 Decision Tree................................................................................................. 7.1 Introduction.......................................................................................... 7.2 Classification Process......................................................................... 7.2.1 Basic Structure...................................................................... 7.2.2 Toy Examples....................................................................... 7.2.3 Text Classification............................................................... 7.2.4 Rule Extraction.................................................................... 7.3 Learning Process................................................................................ 7.3.1 Preprocessing....................................................................... 7.3.2 Root Node............................................................................. 7.3.3 Interior Nodes....................................................................... 7.3.4 Pruning.................................................................................. 7.4 Variants................................................................................................ 7.4.1 Regression Version.............................................................. 7.4.2 Decision List......................................................................... 7.4.3 Random Forest..................................................................... 7.4.4 Decision Graph.................................................................... 7.5 Summary and Further Discussions................................................... Reference........................................................................................................ 141 141 142 142 144 147 148 150 151 152 154 155 156 156 158 160 162 164 165 8 Support VectorMachine.............................................................................. 8.1 Introduction......................................................................................... 8.2 Classification Process........................................................................ 8.2.1 Linear Classifier.................................................................. 8.2.2 Kernel Functions.................................................................. 8.2.3 Lagrange Multipliers.......................................................... 8.2.4 Generalization ..................................................................... 8.3 Learning Process................................................................................ 8.3.1 Primal Problem.................................................................... 8.3.2 Dual Problem....................................................................... 8.3.3 SMO Algorithm.................................................................. 8.3.4 Other Optimization Schemes.............................................. 8.4 Variants............................................................................................... 8.4.1 Fuzzy SVM.......................................................................... 8.4.2 Pairwise SVM ..................................................................... 8.4.3 LMS SVM........................................................................... 8.4.4 Sparse SVM.......................................................................... 8.5 Summary and FurtherDiscussions.................................................... References...................................................................................................... 167 167 168 168 170 171 173 174 174 176 177 180 181 181 182 183 185 186 187 Part III Unsupervised Learning 9 Simple ClusteringAlgorithms..................................................................... 9.1 Introduction......................................................................................... 9.2 AHC Algorithm................................................................................. 9.2.1 Cluster Similarity................................................................... 191 191 192 192 Contents xvii 9.2.2 Initial Version...................................................................... 9.2.3 Fuzzy Clustering.................................................................. 9.2.4 Variants................................................................................. 9.3 Divisive Clustering Algorithm.......................................................... 9.3.1 Binary Clustering................................................................ 9.3.2 Evolutionary Binary Clustering......................................... 9.3.3 Standard Version................................................................. 9.3.4 Variants................................................................................. 9.4 Online Linear Clustering Algorithm............................................... 9.4.1 Representative Selection Scheme...................................... 9.4.2 Initial Version...................................................................... 9.4.3 Fuzzy Clustering................................................................. 9.4.4 Variants................................................................................. 9.5 Summary and Further Discussions.................................................. References...................................................................................................... 194 195 198 200 200 202 204 205 207 208 209 210 212 214 215 10 К Means Algorithm.................................................................................... 10.1 Introduction........................................................................................ 10.2 Supervised and Unsupervised Learning............................................ 10.2.1 Learning Paradigm Transition ........................................... 10.2.2 Unsupervised KNN............................................................. 10.2.3 Semi-supervised KNN........................................................ 10.2.4 Dynamic Data Organization .............................................. 10.3 Clustering Process.............................................................................. 10.3.1 Initialization......................................................................... 10.3.2 Hard Clustering................................................................... 10.3.3 Fuzzy Clustering.................................................................. 10.3.4 Hierarchical Clustering....................................................... 10.4 Variants............................................................................................... 10.4.1 К Medoid Algorithm.......................................................... 10.4.2 Dynamic К Means Algorithm............................................ 10.4.3 Semi-supervised Version.................................................... 10.4.4 Constraint Clustering.......................................................... 10.5 Summary and Further Discussions.................................................. References...................................................................................................... 217 217 218 218 219 221 222 224 224 225 227 229 230 230 233 234 236 239 240 11 EM Algorithm............................................................................................... 11.1 Introduction........................................................................................ 11.2 Cluster Distributions......................................................................... 11.2.1 Uniform Distribution.......................................................... 11.2.2 Gaussian Distribution .......................................................... 11.2.3 Poisson Distribution........................................................... 11.2.4 Fuzzy Distributions............................................................. 11.3 Clustering Process.............................................................................. 11.3.1 Initialization......................................................................... 11.3.2 E-Step.................................................................................. 241 241 242 242 244 245 247 249 249 250 xviii 12 Contents 11.3.3 M-Step.................................................................................. 11.3.4 Issues..................................................................................... 11.4 Semi-Supervised Learning: Text Classification.............................. 11.4.1 Semi-Supervised Learning................................................. 11.4.2 Initialization.......................................................................... 11.4.3 Likelihood Estimation......................................................... 11.4.4 Parameter Estimation........................................................... 11.5 Summary and Further Discussions.................................................... References....................................................................................................... 252 253 254 254 255 256 257 259 260 Advanced Clustering................................................................................... 12.1 Introduction......................................................................................... 12.2 Cluster Index...................................................................................... 12.2.1 Computation Process........................................................... 12.2.2 Hard Clustering Evaluation................................................ 12.2.3 Fuzzy Clustering Evaluation.............................................. 12.2.4 Hierarchical Clustering Evaluation................................... 12.3 Parameter Tuning................................................................................ 12.3.1 Clustering Index to Unlabeled Items................................ 12.3.2 Simple Clustering Algorithms........................................... 12.3.3 К Means Algorithm............................................................ 12.3.4 Evolutionary Clustering...................................................... 12.4 Clustering Governance....................................................................... 12.4.1 Cluster Naming.................................................................... 12.4.2 Cluster Maintenance............................................................ 12.4.3 Multiple Viewed Clustering............................................... 12.4.4 Clustering Results Integration ........................................... 12.5 Summary and Further Discussions................................................... References....................................................................................................... 261 261 262 262 264 265 267 268 268 269 270 271 272 272 274 276 278 280 282 Part IV 13 Advanced Topics Ensemble Learning...................................................................................... 13.1 Introduction......................................................................................... 13.2 Combination Schemes...................................................................... 13.2.1 Voting................................................................................... 13.2.2 Expert Gates........................................................................ 13.2.3 Cascading............................................................................. 13.2.4 Cellular Model..................................................................... 13.3 Meta-learning...................................................................................... 13.3.1 Voting................................................................................... 13.3.2 Expert Gates........................................................................ 13.3.3 Cascading............................................................................. 13.3.4 Cellular Model..................................................................... 13.4 Partition............................................................................................... 13.4.1 Training Set Partition.......................................................... 285 285 286 286 287 289 290 291 292 294 296 298 300 300 Contents xix 13.4.2 Attribute Set Partition........................................................ 13.4.3 Architecture Partition.......................................................... 13.4.4 Parallel and Distributed Learning...................................... 13.5 Summary and Further Discussions................................................. References..................................................................................................... 302 303 304 306 307 14 Semi-supervised Learning......................................................................... 14.1 Introduction........................................................................................ 14.2 Kohonen Networks............................................................................ 14.2.1 InitialVersion...................................................................... 14.2.2 Learning Vector Quantization............................................ 14.2.3 Semi-supervised Version.................................................... 14.2.4 Kohonen Networks vs. К Means Algorithm.................... 14.3 Combined Learning Algorithms....................................................... 14.3.1 Combination Paradigms..................................................... 14.3.2 Simple Learning Algorithms............................................. 14.3.3 К Means Algorithm 4- KNN Algorithm............................ 14.3.4 EM Algorithm 4- Naive Bayes........................................... 14.4 Advanced Supervised Learning........................................................ 14.4.1 Resampling ......................................................................... 14.4.2 Virtual Training Example.................................................. 14.4.3 Co-Learning......................................................................... 14.4.4 Incremental Learning.......................................................... 14.5 Summary and Further Discussions.................................................. References...................................................................................................... 309 309 310 310 313 315 317 318 318 320 323 324 325 326 327 329 331 332 334 15 Temporal Learning..................................................................................... 15.1 Introduction........................................................................................ 15.2 Discrete Markov Model.................................................................... 15.2.1 State Diagram...................................................................... 15.2.2 State Transition Probability............................................... 15.2.3 State Path Probability.......................................................... 15.2.4 Application to Time Series Prediction............................... 15.3 Hidden Markov Model...................................................................... 15.3.1 Initial Parameters................................................................ 15.3.2 Observation Sequence Probability..................................... 15.3.3 State Sequence Estimation................................................. 15.3.4 HMM Learning................................................................... 15.4 Text Topic Analysis.......................................................................... 15.4.1 Task Specification.............................................................. 15.4.2 Sampling............................................................................. 15.4.3 Learning............................................................................... 15.4.4 Topic Sequence................................................................... 15.5 Summary and Further Discussions.................................................. References...................................................................................................... 335 335 336 336 337 338 340 341 342 343 346 349 351 351 353 354 356 357 358 Contents XX 16 Reinforcement Learning.............................................................................. 16.1 Introduction......................................................................................... 16.2 Simple Reinforcement Learning........................................................ 16.2.1 Single Example.................................................................... 16.2.2 Classification......................................................................... 16.2.3 Regression............................................................................ 16.2.4 Autonomous Moving........................................................... 16.3 Q Learning........................................................................................... 16.3.1 Q Table.................................................................................. 16.3.2 Finite State............................................................................ 16.3.3 Infinite State......................................................................... 16.3.4 Stochastic Reward................................................................ 16.4 Advanced Reinforcement Learning.................................................. 16.4.1 Ensemble Reinforcement Learning.................................... 16.4.2 Reinforcement + Supervised............................................... 16.4.3 Reinforcement + Unsupervised.......................................... 16.4.4 Environment Prediction...................................................... 16.5 Summary and Further Discussions................................................... Index 359 359 360 360 362 364 366 368 368 370 371 374 375 376 378 380 382 384 385
adam_txt	Contents Part I Foundation 1 Introduction. 1.1 Definition of Machine Learning. 1.2 Application Areas. 1.2.1 Classification. 1.2.2 Regression. 1.2.3 Clustering. 1.2.4 Hybrid Tasks. 1.3 Machine Learning Types. 1.3.1 Supervised Learning. 1.3.2 Unsupervised Learning. 1.3.3 Semi-supervised Learning. 1.3.4 Reinforcement Learning. 1.4 Related Areas. 1.4.1 Artificial Intelligence. 1.4.2 Neural Networks. 1.4.3 Data Mining. 1.4.4 Soft Computing. 1.5 Summary and Further Discussions. References. 3 3 4 4 6 7 8 11 11 12 14 15 16 17 18 19 20 21 22 2 Numerical Vectors. 2.1 Introduction. 2.2 Operations on Numerical Vectors. 2.2.1 Definition . 2.2.2 Basic Operations. 2.2.3 Inner Product. 2.2.4 Linear Independence . 2.3 Operations on Matrices. 2.3.1 Definition . 2.3.2 Basic Operations. 23 23 24 24 25 26 28 29 30 31 xiii Contents xiv 2.3.3 Multiplication. 2.3.4 Inverse Matrix . Vector and Matrix. 2.4.1 Determinant. 2.4.2 Eigen Value and Vector. 2.4.3 Singular Value Decomposition. 2.4.4 Principal Component Analysis. Summary and Further Discussions. 32 35 37 38 40 42 43 45 Data Encoding. 3.1 Introduction. 3.2 Relational Data. 3.2.1 Basic Concepts. 3.2.2 Relational Database. 3.2.3 Encoding Process. 3.2.4 Encoding Issues. 3.3 Textual Data. 3.3.1 Text Indexing. 3.3.2 Text Encoding. 3.3.3 Dimension Reduction . 3.3.4 Encoding Issues. 3.4 Image Data. 3.4.1 Image File Formats. 3.4.2 Image Matrix. 3.4.3 Encoding Process. 3.4.4 Encoding Issues. 3.5 Summary and Further Discussions. References. 47 47 48 48 50 52 53 55 55 58 60 61 62 63 63 65 66 67 67 Simple Machine Learning Algorithms. 4.1 Introduction. 4.2 Classification. 4.2.1 Binary Classification. 4.2.2 Multiple Classification. 4.2.3 Regression . 4.2.4 Problem Decomposition. 4.3 Simple Classifiers. 4.3.1 Threshold Rule. 4.3.2 Rectangle. 4.3.3 Hyperplane. 4.3.4 Matching Algorithm. 4.4 Linear Classifiers. 4.4.1 Linear Separability . 4.4.2 Hyperplane Equation. 4.4.3 Linear Classification. 4.4.4 Perceptron. 69 69 70 70 71 73 74 76 76 77 79 80 82 82 84 86 87 2.4 2.5 3 4 Contents XV 4.5 Summary and Further Discussions. References. Part II 89 90 Supervised Learning 5 Instance Based Learning. 5.1 Introduction. 5.2 Primitive Instance Based Learning. 5.2.1 Look-Up Example. 5.2.2 Rule Based Approach. 5.2.3 Example Similarity. 5.2.4 One Nearest Neighbor. 5.3 Classification Process. 5.3.1 Notations. 5.3.2 Nearest Neighbors. 5.3.3 Voting. 5.3.4 Attribute Discriminations. 5.4 Variants. 5.4.1 Dynamic Nearest Neighbor. 5.4.2 Concentric Nearest Neighbor. 5.4.3 Hierarchical Nearest Neighbor. 5.4.4 Hub Examples. 5.5 Summary and Further Discussions. References. 93 93 94 94 95 98 99 100 100 101 103 105 106 106 108 110 112 113 115 6 Probabilistic Learning. 6.1 Introduction. 6.2 Bayes Classifier. 6.2.1 Probabilities. 6.2.2 Bayes Rule. 6.2.3 Gaussian Distribution . 6.2.4 Classification. 6.3 Naive Bayes . 6.3.1 Classification. 6.3.2 Learning. 6.3.3 Variants. 6.3.4 Application to Text Classification. 6.4 Bayesian Learning. 6.4.1 Bayesian Networks. 6.4.2 Causal Relation. 6.4.3 Learning Process. 6.4.4 Comparisons. 6.5 Summary and Further Discussions. References. 117 117 118 118 120 121 123 124 124 126 128 129 131 131 133 135 136 138 139 xvi Contents 7 Decision Tree. 7.1 Introduction. 7.2 Classification Process. 7.2.1 Basic Structure. 7.2.2 Toy Examples. 7.2.3 Text Classification. 7.2.4 Rule Extraction. 7.3 Learning Process. 7.3.1 Preprocessing. 7.3.2 Root Node. 7.3.3 Interior Nodes. 7.3.4 Pruning. 7.4 Variants. 7.4.1 Regression Version. 7.4.2 Decision List. 7.4.3 Random Forest. 7.4.4 Decision Graph. 7.5 Summary and Further Discussions. Reference. 141 141 142 142 144 147 148 150 151 152 154 155 156 156 158 160 162 164 165 8 Support VectorMachine. 8.1 Introduction. 8.2 Classification Process. 8.2.1 Linear Classifier. 8.2.2 Kernel Functions. 8.2.3 Lagrange Multipliers. 8.2.4 Generalization . 8.3 Learning Process. 8.3.1 Primal Problem. 8.3.2 Dual Problem. 8.3.3 SMO Algorithm. 8.3.4 Other Optimization Schemes. 8.4 Variants. 8.4.1 Fuzzy SVM. 8.4.2 Pairwise SVM . 8.4.3 LMS SVM. 8.4.4 Sparse SVM. 8.5 Summary and FurtherDiscussions. References. 167 167 168 168 170 171 173 174 174 176 177 180 181 181 182 183 185 186 187 Part III Unsupervised Learning 9 Simple ClusteringAlgorithms. 9.1 Introduction. 9.2 AHC Algorithm. 9.2.1 Cluster Similarity. 191 191 192 192 Contents xvii 9.2.2 Initial Version. 9.2.3 Fuzzy Clustering. 9.2.4 Variants. 9.3 Divisive Clustering Algorithm. 9.3.1 Binary Clustering. 9.3.2 Evolutionary Binary Clustering. 9.3.3 Standard Version. 9.3.4 Variants. 9.4 Online Linear Clustering Algorithm. 9.4.1 Representative Selection Scheme. 9.4.2 Initial Version. 9.4.3 Fuzzy Clustering. 9.4.4 Variants. 9.5 Summary and Further Discussions. References. 194 195 198 200 200 202 204 205 207 208 209 210 212 214 215 10 К Means Algorithm. 10.1 Introduction. 10.2 Supervised and Unsupervised Learning. 10.2.1 Learning Paradigm Transition . 10.2.2 Unsupervised KNN. 10.2.3 Semi-supervised KNN. 10.2.4 Dynamic Data Organization . 10.3 Clustering Process. 10.3.1 Initialization. 10.3.2 Hard Clustering. 10.3.3 Fuzzy Clustering. 10.3.4 Hierarchical Clustering. 10.4 Variants. 10.4.1 К Medoid Algorithm. 10.4.2 Dynamic К Means Algorithm. 10.4.3 Semi-supervised Version. 10.4.4 Constraint Clustering. 10.5 Summary and Further Discussions. References. 217 217 218 218 219 221 222 224 224 225 227 229 230 230 233 234 236 239 240 11 EM Algorithm. 11.1 Introduction. 11.2 Cluster Distributions. 11.2.1 Uniform Distribution. 11.2.2 Gaussian Distribution . 11.2.3 Poisson Distribution. 11.2.4 Fuzzy Distributions. 11.3 Clustering Process. 11.3.1 Initialization. 11.3.2 E-Step. 241 241 242 242 244 245 247 249 249 250 xviii 12 Contents 11.3.3 M-Step. 11.3.4 Issues. 11.4 Semi-Supervised Learning: Text Classification. 11.4.1 Semi-Supervised Learning. 11.4.2 Initialization. 11.4.3 Likelihood Estimation. 11.4.4 Parameter Estimation. 11.5 Summary and Further Discussions. References. 252 253 254 254 255 256 257 259 260 Advanced Clustering. 12.1 Introduction. 12.2 Cluster Index. 12.2.1 Computation Process. 12.2.2 Hard Clustering Evaluation. 12.2.3 Fuzzy Clustering Evaluation. 12.2.4 Hierarchical Clustering Evaluation. 12.3 Parameter Tuning. 12.3.1 Clustering Index to Unlabeled Items. 12.3.2 Simple Clustering Algorithms. 12.3.3 К Means Algorithm. 12.3.4 Evolutionary Clustering. 12.4 Clustering Governance. 12.4.1 Cluster Naming. 12.4.2 Cluster Maintenance. 12.4.3 Multiple Viewed Clustering. 12.4.4 Clustering Results Integration . 12.5 Summary and Further Discussions. References. 261 261 262 262 264 265 267 268 268 269 270 271 272 272 274 276 278 280 282 Part IV 13 Advanced Topics Ensemble Learning. 13.1 Introduction. 13.2 Combination Schemes. 13.2.1 Voting. 13.2.2 Expert Gates. 13.2.3 Cascading. 13.2.4 Cellular Model. 13.3 Meta-learning. 13.3.1 Voting. 13.3.2 Expert Gates. 13.3.3 Cascading. 13.3.4 Cellular Model. 13.4 Partition. 13.4.1 Training Set Partition. 285 285 286 286 287 289 290 291 292 294 296 298 300 300 Contents xix 13.4.2 Attribute Set Partition. 13.4.3 Architecture Partition. 13.4.4 Parallel and Distributed Learning. 13.5 Summary and Further Discussions. References. 302 303 304 306 307 14 Semi-supervised Learning. 14.1 Introduction. 14.2 Kohonen Networks. 14.2.1 InitialVersion. 14.2.2 Learning Vector Quantization. 14.2.3 Semi-supervised Version. 14.2.4 Kohonen Networks vs. К Means Algorithm. 14.3 Combined Learning Algorithms. 14.3.1 Combination Paradigms. 14.3.2 Simple Learning Algorithms. 14.3.3 К Means Algorithm 4- KNN Algorithm. 14.3.4 EM Algorithm 4- Naive Bayes. 14.4 Advanced Supervised Learning. 14.4.1 Resampling . 14.4.2 Virtual Training Example. 14.4.3 Co-Learning. 14.4.4 Incremental Learning. 14.5 Summary and Further Discussions. References. 309 309 310 310 313 315 317 318 318 320 323 324 325 326 327 329 331 332 334 15 Temporal Learning. 15.1 Introduction. 15.2 Discrete Markov Model. 15.2.1 State Diagram. 15.2.2 State Transition Probability. 15.2.3 State Path Probability. 15.2.4 Application to Time Series Prediction. 15.3 Hidden Markov Model. 15.3.1 Initial Parameters. 15.3.2 Observation Sequence Probability. 15.3.3 State Sequence Estimation. 15.3.4 HMM Learning. 15.4 Text Topic Analysis. 15.4.1 Task Specification. 15.4.2 Sampling. 15.4.3 Learning. 15.4.4 Topic Sequence. 15.5 Summary and Further Discussions. References. 335 335 336 336 337 338 340 341 342 343 346 349 351 351 353 354 356 357 358 Contents XX 16 Reinforcement Learning. 16.1 Introduction. 16.2 Simple Reinforcement Learning. 16.2.1 Single Example. 16.2.2 Classification. 16.2.3 Regression. 16.2.4 Autonomous Moving. 16.3 Q Learning. 16.3.1 Q Table. 16.3.2 Finite State. 16.3.3 Infinite State. 16.3.4 Stochastic Reward. 16.4 Advanced Reinforcement Learning. 16.4.1 Ensemble Reinforcement Learning. 16.4.2 Reinforcement + Supervised. 16.4.3 Reinforcement + Unsupervised. 16.4.4 Environment Prediction. 16.5 Summary and Further Discussions. Index 359 359 360 360 362 364 366 368 368 370 371 374 375 376 378 380 382 384 385
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id	DE-604.BV047219423
illustrated	Not Illustrated
index_date	2024-07-03T16:56:56Z
indexdate	2024-07-10T09:05:59Z
institution	BVB
isbn	9783030658991
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-032624068
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physical	xx, 391 Seiten Diagramme
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publisher	Springer
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spelling	Jo, Taeho Verfasser (DE-588)1221820869 aut Machine learning foundations supervised, unsupervised, and advanced learning Taeho Jo Cham, Switzerland Springer [2021] xx, 391 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics Electrical engineering Computational intelligence Data mining Information storage and retrieval Big data Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-65900-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032624068&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Jo, Taeho Machine learning foundations supervised, unsupervised, and advanced learning Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics Electrical engineering Computational intelligence Data mining Information storage and retrieval Big data Maschinelles Lernen (DE-588)4193754-5 gnd
subject_GND	(DE-588)4193754-5
title	Machine learning foundations supervised, unsupervised, and advanced learning
title_auth	Machine learning foundations supervised, unsupervised, and advanced learning
title_exact_search	Machine learning foundations supervised, unsupervised, and advanced learning
title_exact_search_txtP	Machine learning foundations supervised, unsupervised, and advanced learning
title_full	Machine learning foundations supervised, unsupervised, and advanced learning Taeho Jo
title_fullStr	Machine learning foundations supervised, unsupervised, and advanced learning Taeho Jo
title_full_unstemmed	Machine learning foundations supervised, unsupervised, and advanced learning Taeho Jo
title_short	Machine learning foundations
title_sort	machine learning foundations supervised unsupervised and advanced learning
title_sub	supervised, unsupervised, and advanced learning
topic	Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics Electrical engineering Computational intelligence Data mining Information storage and retrieval Big data Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet	Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics Electrical engineering Computational intelligence Data mining Information storage and retrieval Big data Maschinelles Lernen
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032624068&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT jotaeho machinelearningfoundationssupervisedunsupervisedandadvancedlearning

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