Elements of Machine LearningRecent years have seen an explosion of work on machine learning, the computational study of algorithms that improve performance based on experience. Research on rule induction, neural networks, genetic algorithms, case-based reasoning, and probabilistic inference has produced a variety of robust methods for inducing knowledge from training data. This book covers the main induction algorithms explored in the literature and presents them within a coherent theoretical framework that moves beyond traditional paradigm boundaries. Elements of Machine Learning provides a comprehensive introduction to the fundamental concepts and problems in the field. The book illustrates a variety of basic algorithms for inducing simple concepts from experience, presents alternatives for organizing learned concepts into large-scale structures, and discusses adaptations of the learning methods to more complex problem-solving tasks. The chapters describe these computational techniques in detail and give examples of their operation, along with exercises and references to the literature. This text is suitable for use in graduate courses on machine learning. Researchers and students in artificial intelligence |
Contents
An overview of machine learning | 1 |
The induction of logical conjunctions | 27 |
The induction of threshold concepts | 67 |
The induction of competitive concepts | 95 |
The construction of decision lists | 115 |
Revision and extension of inference networks | 149 |
The formation of concept hierarchies | 187 |
Other issues in concept induction | 227 |
The formation of transition networks | 257 |
The acquisition of searchcontrol knowledge | 289 |
The formation of macrooperators | 331 |
Prospects for machine learning | 377 |
| 389 | |
| 415 | |
Common terms and phrases
approach arm-empty Artificial Intelligence attributes Bayesian classifier beam search behavior block Chapter clear cluster component compute concept descriptions concept hierarchies consider decision boundaries decision list decision regions decision tree disjunctive domains evaluation function example extensional definitions Figure finite-state framework genetic algorithms girth goal grammar heuristic hill-climbing HSET hypothesis incremental inference network initial instance space involves ISET learner learning algorithms lethargia linear threshold units literals logical conjunctions machine learning macro-operators maketower match means-ends means-ends analysis methods Morgan Kaufmann moveupto negative instances neoplasm nonincremental nuclei one-nucleus operator parse tree pickup positive instances prediction probabilistic problem solver produce prototype PSET putdown recursive transition networks representation researchers result revised rules scheme score sequence simploid solution path solving specific stored subnetworks subproblem Table tails target concept task techniques terminal nodes thick-wall tion training data training instances training set typically unstack unsupervised learning values weights