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CITU |
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20230925121213.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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090219s2009 flua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2009007292 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781420067187 (hardcover : alk. paper) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1420067184 (hardcover : alk. paper) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)ocn156812741 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
BTCTA |
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BAKER |
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YDXCP |
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YDX |
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C#P |
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CDX |
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BWX |
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DLC |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q325.5 |
| Item number |
.M368 2009 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3/1 |
| Edition number |
22 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Marsland, Stephen. |
| 245 10 - TITLE STATEMENT |
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| Title |
Machine learning : |
| Remainder of title |
an algorithmic perspective / |
| Statement of responsibility, etc. |
Stephen Marsland. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Boca Raton : |
| Name of publisher, distributor, etc. |
CRC Press, |
| Date of publication, distribution, etc. |
c2009. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 390 p. : |
| Other physical details |
ill. ; |
| Dimensions |
25 cm. |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Chapman & Hall/CRC machine learning & pattern recognition series |
| 500 ## - GENERAL NOTE |
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| General note |
"A Chapman & Hall book." |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1 Introduction 1<br/>1.1 If Data Had Mass, the Earth Would Be a Black Hole . . .. 2<br/>1.2 Learning . ............................ 4<br/>1.2.1 Machine Learning ................. ... 5<br/>1.3 Types of Machine Learning ................... 6<br/>1.4 Supervised Learning ....................... 7<br/>1.4.1 Regression ................... ...... 8<br/>1.4.2 Classification ....................... 9<br/>1.5 The Brain and the Neuron ................... 11<br/>1.5.1 Hebb's Rule ........ ............... 12<br/>1.5.2 McCulloch and Pitts Neurons . ............ 13<br/>1.5.3 Limitations of the McCulloch and Pitt Neuronal Model 15<br/>Further Reading . ........................... 16<br/>2 Linear Discriminants 17<br/>2.1 Preliminaries ........................... 18<br/>2.2 The Perceptron ................... ...... 19<br/>2.2.1 The Learning Rate rj ................... 21<br/>2.2.2 The Bias Input ................... ... 22<br/>2.2.3 The Perceptron Learning Algorithm . ......... 23<br/>2.2.4 An Example of Perceptron Learning . ......... 24<br/>2.2.5 Implementation ................... ... 26<br/>2.2.6 Testing the Network . .................. 31<br/>2.3 Linear Separability ................... .... 32<br/>2.3.1 The Exclusive Or (XOR) Function . .......... 34<br/>2.3.2 A Useful Insight ................... .. 36<br/>2.3.3 Another Example: The Pima Indian Dataset .... . 37<br/>2.4 Linear Regression ............ ........... 41<br/>2.4.1 Linear Regression Examples . .............. 43<br/>Further Reading ................... ....... .. 44<br/>Practice Questions ................... ...... . 45<br/>3 The Multi-Layer Perceptron 47<br/>3.1 Going Forwards ................ .......... 49<br/>3.1.1 Biases .............. ..... ...... . 50<br/>3.2 Going Backwards: Back-Propagation of Error . ....... 50<br/>3.2.1 The Multi-Layer Perceptron Algorithm . . .. .... 54<br/>3.2.2 Initialising the Weights . ...... .......... 57<br/>3.2.3 Different Output Activation Functions ... . ...... 58<br/>3.2.4 Sequential and Batch Training . ............ 59<br/>3.2.5 Local Minima . .......... . . ... . 60<br/>3.2.6 Picking Up Momentum ......... ....... 61<br/>3.2.7 Other Improvements . .................. 62<br/>3.3 The Multi-Layer Perceptron in Practice . ........... 63<br/>3.3.1 Data Preparation .... ................ 63<br/>3.3.2 Amount of Training Data . ............... 63<br/>3.3.3 Number of Hidden Layers . ............... 64<br/>3.3.4 Generalisation and Overfitting . ............ 66<br/>3.3.5 Training, Testing, and Validation . ........... 66<br/>3.3.6 When to Stop Learning ... .............. 68<br/>3.3.7 Computing and Evaluating the Results . ....... 69<br/>3.4 Examples of Using the MLP .................. 70<br/>3.4.1 A Regression Problem ... .... ........... 70<br/>3.4.2 Classification with the MLP . .............. 74<br/>3.4.3 A Classification Example .. . . . . . . ........ . 75<br/>3.4.4 Time-Series Prediction . . .. .. . . ....... . 77<br/>3.4.5 Data Compression: The Auto-Associative Network . 80<br/>3.5 Overview ................. ........... ..83<br/>3.6 Deriving Back-Propagation ................... 84<br/>3.6.1 The Network Output and the Error . . ..... ... 884<br/>3.6.2 The Error of the Network ..... ....... . . 85<br/>3.6.3 A Suitable Activation Function . ........... 87<br/>3.6.4 Back-Propagation of Error . ......... . .. . . 88<br/>Further Reading ...... .. . . .......... ... . 90<br/>Practice Questions . . . . . . ............... ... ... 91<br/>4 Radial Basis Functions and Splines 95<br/>4.1 Concepts ................. ......... .. 95<br/>4.1.1 Weight Space ......... ............. 95<br/>4.1.2 Receptive Fields . ....... .... .. .. . . 97<br/>4.2 The Radial Basis Function (RBF" Network . ......... 100<br/>4.2.1 Training the RBF Network . ......... . . . 103<br/>4.3 The Curse of Dimensionality .... ........... . . 106<br/>4.4 Interpolation and Basis Functions ... .... ........ 108<br/>4.4.1 Bases and Basis Expansion ............ ... 108<br/>4.4.2 The Cubic Spline . ... ................ 112<br/>4.4.3 Fitting the Spline to the Data ........ ....... 112<br/>4.4.4 Smoothing Splines .... ................ 113<br/>4.4.5 Higher Dimensions ...... ... ........... 114<br/>4.4.6 Beyond the Bounds ................... . 116<br/>Further Reading ................... .... ... . 116<br/>Practice Questions ................... ........ 117<br/>5 Support Vector Machines 119<br/>5.1 Optimal Separation ............. ......... 120<br/>5.2 Kernels .... ....... . ................... 125<br/>5.2.1 Example: XOR .. ................... . 128<br/>5.2.2 Extensions to the Support Vector Machine . . .... 128<br/>Further Reading ................... ....... . 130<br/>Practice Questions .... . .. ........ .. .......... 131<br/>6 Learning with Trees 133<br/>6.1 Using Decision Trees ............ . ........ 133<br/>6.2 Constructing Decision Trees . ................. 134<br/>6.2.1 Quick Aside: Entropy in Information Theory .... . 135<br/>6.2.2 ID3 ...... ......... ... ......... 136<br/>6.2.3 Implementing Trees and Graphs in Python ...... 139<br/>6.2.4 Implementation of the Decision Tree . ......... 140<br/>6.2.5 Dealing with Continuous Variables .......... . 143<br/>6.2.6 Computational Complexity . .............. 143<br/>6.3 Classification and Regression Trees (CART) . ........ 145<br/>6.3.1 Gini Impurity ................... .. . 146<br/>6.3.2 Regression in Trees .................. .. 147<br/>6.4 Classification Example ........ ............. 147<br/>Further Reading .... ................... .. . . 150<br/>Practice Questions ................... ........ 151<br/>7 Decision by Committee: Ensemble Learning 153<br/>7.1 Boosting ................ .......... ..154<br/>7.1.1 AdaBoost ......... . ................ 155<br/>7.1.2 Stumping ......... ................. 160<br/>7.2 Bagging .............................. 160<br/>7.2.1 Subagging ................... ...... 162<br/>7.3 Different Ways to Combine Classifiers . ............ . 162<br/>Further Reading ................... ....... .. 164<br/>Practice Questions ................... ........ 165<br/>8 Probability and Learning 167<br/>8.1 Turning Data into Probabilities . ............... 167<br/>8.1.1 Minimising Risk . ........ ........ ... 171<br/>8.1.2 The Naive Bayes' Classifier . .............. 171<br/>8.2 Some Basic Statistics ................ ...... 173<br/>8.2.1 Averages ......................... 173<br/>8.2.2 Variance and Covariance . ................ 174<br/>8.2.3 The Gaussian ................... .... 176<br/>8.2.4 The Bias-Variance Tradeoff .. .............. 177<br/>8.3 Gaussian Mixture Models ................... . 178<br/>8.3.1 The Expectation-Maximisation (EM) Algorithm . . . 179<br/>8.4 Nearest Neighbour Methods . ................. 183<br/>8.4.1 Nearest Neighbour Smoothing . ............. 185<br/>8.4.2 Efficient Distance Computations: the KD-Tree . . . . 186<br/>8.4.3 Distance Measures ................... . 190<br/>Further Reading ................... ......... 192<br/>Practice Questions ....... .......... ........ 193<br/>9 Unsupervised Learning 195<br/>9.1 The k-Means Algorithm ................... .. 196<br/>9.1.1 Dealing with Noise ........ ............ 200<br/>9.1.2 The k-Means Neural Network . ............. 200<br/>9.1.3 Normalisation ................... .... 202<br/>9.1.4 A Better Weight Update Rule ............. . 203<br/>9.1.5 Example: The Iris Dataset Again ...... ...... 204<br/>9.1.6 Using Competitive Learning for Clustering ...... 205<br/>9.2 Vector Quantisation ....................... 206<br/>9.3 The Self-Organising Feature Map . .............. 207<br/>9.3.1 The SOM Algorithm . .................. 210<br/>9.3.2 Neighbourhood Connections . ............. 211<br/>9.3.3 Self-Organisation ................... .. 214<br/>9.3.4 Network Dimensionality and Boundary Conditions . 214<br/>9.3.5 Examples of Using the SOM . ............. 215<br/>Further Reading ................... ......... 218<br/>Practice Questions ................... ........ 220<br/>10 Dimensionality Reduction 221<br/>10.1 Linear Discriminant Analysis (LDA) . ............. 223<br/>10.2 Principal Components Analysis (PCA) . ........... 226<br/>10.2.1 Relation with the Multi-Layer Perceptron ....... 231<br/>10.2.2 Kernel PCA ................... ..... 232<br/>10.3 Factor Analysis ................... ...... 234<br/>10.4 Independent Components Analysis (ICA) . .......... 237<br/>10.5 Locally Linear Embedding . .................. 239<br/>10.6 Isomap .......... ................. 242<br/>10.6.1 Multi-Dimensional Scaling (MDS) .......... . 242<br/>Further Reading ................. ......... .. 245<br/>Practice Questions ................... ...... . 246<br/>11 Optimisation and Search 247<br/>11.1 Going Downhill ........................... 248<br/>11.2 Least-Squares Optimisation ........ ............ 251<br/>11.2.1 Taylor Expansion ................... .. 251<br/>11.2.2 The Levenberg-Marquardt Algorithm . ........ 252<br/>11.3 Conjugate Gradients ................... .... 257<br/>11.3.1 Conjugate Gradients Example . ............ 260<br/>11.4 Search: Three Basic Approaches . ............... 261<br/>11.4.1 Exhaustive Search . ................... 261<br/>11.4.2 Greedy Search ................... ... 262<br/>11.4.3 Hill Climbing ................... .. . 262<br/>11.5 Exploitation and Exploration . ................. 264<br/>11.6 Simulated Annealing .. .................... 265<br/>11.6.1 Comparison ................... ..... 266<br/>Further Reading . ........ ................... 267<br/>Practice Questions ................... ........ 267<br/>12 Evolutionary Learning 269<br/>12.1 The Genetic Algorithm (GA) ........... ..... . 270<br/>12.1.1 String Representation . ................. 271<br/>12.1.2 Evaluating Fitness ................... . 272<br/>12.1.3 Population ... ..................... 273<br/>12.1.4 Generating Offspring: Parent Selection . ........ 273<br/>12.2 Generating Offspring: Genetic Operators . .......... 275<br/>12.2.1 Crossover ......................... 275<br/>12.2.2 Mutation . ........................ 277<br/>12.2.3 Elitism, Tournaments, and Niching . .......... 277<br/>12.3 Using Genetic Algorithms ................... . . 279<br/>12.3.1 Map Colouring ................... ... 279<br/>12.3.2 Punctuated Equilibrium . ................ 281<br/>12.3.3 Example: The Knapsack Problem . .......... 281<br/>12.3.4 Example: The Four Peaks Problem . .......... 282<br/>12.3.5 Limitations of the GA . ................. 284<br/>12.3.6 Training Neural Networks with Genetic Algorithms.. 285<br/>12.4 Genetic Programming ................... ... 285<br/>12.5 Combining Sampling with Evolutionary Learning . ..... 286<br/>Further Reading ................... ....... . 289<br/>Practice Questions ................... ...... . 290<br/>13 Reinforcement Learning 293<br/>13.1 Overview ................... ......... 294<br/>13.2 Example: Getting Lost ................... . . 296<br/>13.2.1 State and Action Spaces . ................ 298<br/>13.2.2 Carrots and Sticks: the Reward Function . ...... 299<br/>13.2.3 Discounting ................... ..... 300<br/>13.2.4 Action Selection .......... ........... 301<br/>13.2.5 Policy ................ ......... ..302<br/>13.3 Markov Decision Processes ........ ........... 302<br/>13.3.1 The Markov Property . ................. 302<br/>13.3.2 Probabilities in Markov Decision Processes . ..... 303<br/>13.4 Values ............ .. ....... . ... . ...... 305<br/>13.5 Back on Holiday: Using Reinforcement Learning . ...... 309<br/>13.6 The Difference between Sarsa and Q-Learning . ....... 310<br/>13.7 Uses of Reinforcement Learning . ............... 311<br/>Further Reading ................. ......... ..312<br/>Practice Questions ................ . ........ 312<br/>14 Markov Chain Monte Carlo (MCMC) Methods 315<br/>14.1 Sampling .................. . ........ ..315<br/>14.1.1 Random Numbers ......... .......... 316<br/>14.1.2 Gaussian Random Numbers . .............. 317<br/>14.2 Monte Carlo or Bust ........... . ........ 319<br/>14.3 The Proposal Distribution ........ .......... 320<br/>14.4 Markov Chain Monte Carlo . .................. 325<br/>14.4.1 Markov Chains ........... ........... 325<br/>14.4.2 The Metropolis-Hastings Algorithm .... ...... . 326<br/>14.4.3 Simulated Annealing (Again) . ............. 327<br/>14.4.4 Gibbs Sampling ............ ........... 328<br/>Further Reading .................. .......... 331<br/>Practice Questions ................ ......... ..332<br/>15 Graphical Models 333<br/>15.1 Bayesian Networks ............. ......... . 335<br/>15.1.1 Example: Exam Panic ....... . .......... 335<br/>15.1.2 Approximate Inference ........ ........... 339<br/>15.1.3 Making Bayesian Networks . .............. 342<br/>15.2 Markov Random Fields ................... .. 344<br/>15.3 Hidden Markov Models (HMMs) ..... . .......... 347<br/>15.3.1 The Forward Algorithm ...... ........... 349<br/>15.3.2 The Viterbi Algorithm ................. . 352<br/>15.3.3 The Baum-Welch or Forward-Backward Algorithm . 353<br/>15.4 Tracking Methods ............. . ........ 356<br/>15.4.1 The Kalman Filter .................. .. 357<br/>15.4.2 The Particle Filter ......... ........... 360<br/>Further Reading ................. .......... . 361<br/>Practice Questions ................ ......... . 362<br/>16 Python 365<br/>16.1 Installing Python and Other Packages ......... . .. 365<br/>16.2 Getting Started . . . . . . .............. . . . 365<br/>16.2.1 Python for MATLAB and R users . .......... 370<br/>16.3 Code Basics ...... . ....... . .............. 370<br/>16.3.1 Writing and Importing Code . . ............ 370<br/>16.3.2 Control Flow . ........ ............. . 371<br/>16.3.3 Functions . . . .... . ...... . . . . . 372<br/>16.3.4 The doc String . . .............. . . ... .... 373<br/>16.3.5 map and lambda . . . .. ........... . ... ..373<br/>16.3.6 Exceptions . ........ . ...... . . . . . . . 374<br/>16.3.7 Classes . . . . . . . . ............. ... . 374<br/>16.4 Using NumPy and Matplotlib . ........ . . . . . 375<br/>16.4.1 Arrays ........... .. ............ . 375<br/>16.4.2 Random Numbers ............ . . . . . .....379<br/>16.4.3 Linear Algebra ....... .. .... . . .......... 379<br/>16.4.4 Plotting . . . ............ . .. . ...... . 380<br/>Further Reading .. ... .............. ......... . . 381<br/>Practice Questions ..... ................. .. . . . . . . 382<br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Algorithms. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
Chapman & Hall/CRC machine learning & pattern recognition series. |
| 856 41 - ELECTRONIC LOCATION AND ACCESS |
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| Materials specified |
Table of contents only |
| Uniform Resource Identifier |
<a href="http://www.loc.gov/catdir/toc/fy0904/2009007292.html">http://www.loc.gov/catdir/toc/fy0904/2009007292.html</a> |
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BOOK |
| Koha issues (borrowed), all copies |
2 |