An Introduction to Machine Learning (2nd ed. 2017)

An Introduction to Machine Learning (2nd ed. 2017)

By: Miroslav Kubat (author)Hardback

Special OrderSpecial Order item not currently available. We'll try and order for you.

Description

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

About Author

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks.

Contents

1 A Simple Machine-Learning Task 1 1.1 Training Sets and Classifiers.......................................................................... 1 1.2 Minor Digression: Hill-Climbing Search....................................................... 5 1.3 Hill Climbing in Machine Learning................................................................ 9 1.4 The Induced Classifier's Performance........................................................ 12 1.5 Some Difficulties with Available Data......................................................... 14 1.6 Summary and Historical Remarks............................................................... 18 1.7 Solidify Your Knowledge.............................................................................. 19 2 Probabilities: Bayesian Classifiers 22 2.1 The Single-Attribute Case............................................................................. 22 2.2 Vectors of Discrete Attributes..................................................................... 27 2.3 Probabilities of Rare Events: Exploiting the Expert's Intuition............. 29 2.4 How to Handle Continuous Attributes....................................................... 35 2.5 Gaussian "Bell" Function: A Standard pdf................................................. 38 2.6 Approximating PDFs with Sets of Gaussians............................................ 40 2.7 Summary and Historical Remarks............................................................... 43 2.8 Solidify Your Knowledge.............................................................................. 46 3 Similarities: Nearest-Neighbor Classifiers 49 3.1 The k-Nearest-Neighbor Rule...................................................................... 49 3.2 Measuring Similarity...................................................................................... 52 3.3 Irrelevant Attributes and Scaling Problems............................................... 56 3.4 Performance Considerations........................................................................ 60 3.5 Weighted Nearest Neighbors....................................................................... 63 3.6 Removing Dangerous Examples.................................................................. 65 3.7 Removing Redundant Examples.................................................................. 68 3.8 Summary and Historical Remarks............................................................... 71 3.9 Solidify Your Knowledge.............................................................................. 72 4 Inter-Class Boundaries: Linear and Polynomial Classifiers 75 4.1 The Essence..................................................................................................... 75 4.2 The Additive Rule: Perceptron Learning.................................................... 79 4.3 The Multiplicative Rule: WINNOW............................................................ 85 4.4 Domains with More than Two Classes........................................................ 88 4.5 Polynomial Classifiers..................................................................................... 91 4.6 Specific Aspects of Polynomial Classifiers................................................... 93 4.7 Numerical Domains and Support Vector Machines................................... 97 4.8 Summary and Historical Remarks.............................................................. 100 4.9 Solidify Your Knowledge............................................................................. 101 5 Artificial Neural Networks 105 5.1 Multilayer Perceptrons as Classifiers.......................................................... 105 5.2 Neural Network's Error............................................................................... 110 5.3 Backpropagation of Error........................................................................... 111 5.4 Special Aspects of Multilayer Perceptrons................................................ 117 5.5 Architectural Issues...................................................................................... 121 5.6 Radial Basis Function Networks................................................................. 123 5.7 Summary and Historical Remarks.............................................................. 126 5.8 Solidify Your Knowledge............................................................................. 128 6 Decision Trees 130 6.1 Decision Trees 6.2 Induction of Decision Trees........................................................................ 134 6.3 How Much Information Does an Attribute Convey?............................... 137 6.4 Binary Split of a Numeric Attribute.......................................................... 142 6.5 Pruning.......................................................................................................... 144 6.6 Converting the Decision Tree into Rules.................................................. 149 6.7 Summary and Historical Remarks.............................................................. 151 6.8 Solidify Your Knowledge............................................................................. 153 7 Computational Learning Theory 157 7.1 PAC Learning................................................................................................. 157 7.2 Examples of PAC Learnability.................................................................... 161 7.3 Some Practical and Theoretical Consequences......................................... 164 7.4 VC-Dimension and Learnability................................................................. 166 7.5 Summary and Historical Remarks.............................................................. 169 7.6 Exercises and Thought Experiments......................................................... 170 8 A Few Instructive Applications 173 8.1 Character Recognition................................................................................ 173 8.2 Oil-Spill Recognition.................................................................................... 177 8.3 Sleep Classification...................................................................................... 181 8.4 Brain-Computer Interface.......................................................................... 185 8.5 Medical Diagnosis........................................................................................ 189 8.6 Text Classification........................................................................................ 192 8.7 Summary and Historical Remarks............................................................ 194 8.8 Exercises and Thought Experiments........................................................ 195 9 Induction of Voting Assemblies 198 9.1 Bagging.......................................................................................................... 198 9.2 Schapire's Boosting..................................................................................... 201 9.3 Adaboost: Practical Version of Boosting................................................. <205 9.4 Variations on the Boosting Theme........................................................... 210 9.5 Cost-Saving Benefits of the Approach...................................................... 213 9.6 Summary and Historical Remarks............................................................ 215 9.7 Solidify Your Knowledge............................................................................ 216 10 Some Practical Aspects to Know About 219 10.1 A Learner's Bias.......................................................................................... 219 10.2 Imbalanced Training Sets........................................................................... 223 10.3 Context-Dependent Domains..................................................................... 228 10.4 Unknown Attribute Values......................................................................... 231 10.5 Attribute Selection....................................................................................... 234 10.6 Miscellaneous............................................................................................... 237 10.7 Summary and Historical Remarks............................................................ 238 10.8 Solidify Your Knowledge............................................................................ 240 11 Performance Evaluation 243 11.1 Basic Performance Criteria........................................................................ 243 11.2 Precision and Recall.................................................................................... 247 11.3 Other Ways to Measure Performance..................................................... 252 11.4 Learning Curves and Computational Costs............................................. 255 11.5 Methodologies of Experimental Evaluation............................................. 258 11.6 Summary and Historical Remarks............................................................ 261 11.7 Solidify Your Knowledge............................................................................ 263 12 Statistical Significance 266 12.1 Sampling a Population................................................................................ 266 12.2 Benefiting from the Normal Distribution................................................ 271 12.3 Confidence Intervals................................................................................... 275 12.4 Statistical Evaluation of a Classifier.......................................................... 277 12.5 Another Kind of Statistical Evaluation..................................................... 280 12.6 Comparing Machine-Learning Techniques.............................................. 281 12.7 Summary and Historical Remarks............................................................ 284 12.8 Solidify Your Knowledge............................................................................ 285< 13 Induction in Multi-Label Domains 287 13.1 Classical Machine Learning in Multi-Label Domains................................................................................... 287 13.2 Treating Each Class Separately: Binary Relevance......................................................................................... 290 13.3 Classifier Chains........................................................................................... 293 13.4 Another Possibility: Stacking..................................................................... 296 13.5 A Note on Hierarchically Ordered Classes............................................... 298 13.6 Aggregating the Classes.............................................................................. 301 13.7 Criteria for Performance Evaluation........................................................ 304 13.8 Summary and Historical Remarks............................................................ 307 13.9 Solidify Your Knowledge............................................................................ 308 14 Unsupervised Learning 311 14.1 Cluster Analysis........................................................................................... 311 14.2 A Simple Algorithm: k-Means.................................................................... 315 14.3 More Advanced Versions of k-Means...................................................... 321 14.4 Hierarchical Aggregation............................................................................ 323 14.5 Self-Organizing Feature Maps: Introduction........................................... 326 14.6 Some Important Details.............................................................................. 329 14.7 Why Feature Maps?.................................................................................... 332 14.8 Summary and Historical Remarks............................................................ 334 14.9 Solidify Your Knowledge............................................................................ 335 15 Classifiers in the Form of Rulesets 338 15.1 A Class Described By Rules....................................................................... 338 15.2 Inducing Rulesets by Sequential Covering............................................... 341 15.3 Predicates and Recursion.......................................................................... 344 15.4 More Advanced Search Operators............................................................ 347 15.5 Summary and Historical Remarks.............................................................. 349 15.6 Solidify Your Knowledge............................................................................ 350 16 The Genetic Algorithm< 352< 16.1 The Baseline Genetic Algorithm................................................................ 352 16.2 Implementing the Individual Modules...................................................... 355 16.3 Why it Works............................................................................................... 359 16.4 The Danger of Premature Degeneration................................................. 362 16.5 Other Genetic Operators............................................................................ 364 16.6 Some Advanced Versions........................................................................... 367 16.7 Selections in k-NN Classifiers..................................................................... 370 16.8 Summary and Historical Remarks............................................................ 373 16.9 Solidify Your Knowledge............................................................................ 374 17 Reinforcement Learning 376 17.1 How to Choose the Most Rewarding Action........................................... 376 17.2 States and Actions in a Game.................................................................... 379 17.3 The SARSA Approach................................................................................. 383 17.4 Summary and Historical Remarks............................................................ 384 17.5 Solidify Your Knowledge............................................................................ 384 Index 395

Product Details

  • ISBN13: 9783319639123
  • Format: Hardback
  • Number Of Pages: 348
  • ID: 9783319639123
  • weight: 6682
  • ISBN10: 3319639129
  • edition: 2nd ed. 2017

Delivery Information

  • Saver Delivery: Yes
  • 1st Class Delivery: Yes
  • Courier Delivery: Yes
  • Store Delivery: Yes

Prices are for internet purchases only. Prices and availability in WHSmith Stores may vary significantly

Close