Elements of Machine Learning
Recent 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, cognitive science, and statistics will find it a useful and informative addition to their libraries.
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An overview of machine learning
The induction of logical conjunctions
The induction of threshold concepts
The induction of competitive concepts
The construction of decision lists
Revision and extension of inference networks
The formation of concept hierarchies
Other issues in concept induction