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INTRODUCTION
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Ever since computers were invented, we have wondered whether they might be made to learn If we could understand how to program them to learn-to improve automatically with experience-the impact would be dramatic Imagine computers learning from medical records which treatments are most effective for new diseases, houses learning from experience to optimize energy costs based on the particular usage patterns of their occupants, or personal software assistants learning the evolving interests of their users in order to highlight especially relevant stories from the online morning newspaper A successful understanding of how to make computers learn would open up many new uses of computers and new levels of competence and customization And a detailed understanding of informationprocessing algorithms for machine learning might lead to a better understanding of human learning abilities (and disabilities) as well We do not yet know how to make computers learn nearly as well as people learn However, algorithms have been invented that are effective for certain types of learning tasks, and a theoretical understanding of learning is beginning to emerge Many practical computer programs have been developed to exhibit useful types of learning, and significant commercial applications have begun to appear For problems such as speech recognition, algorithms based on machine learning outperform all other approaches that have been attempted to date In the field known as data mining, machine learning algorithms are being used routinely to discover valuable knowledge from large commercial databases containing equipment maintenance records, loan applications, financial transactions, medical records, and the like As our understanding of computers continues to mature, it
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MACHINE LEARNING
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seems inevitable that machine learning will play an increasingly central role in computer science and computer technology A few specific achievements provide a glimpse of the state of the art: programs have been developed that successfully learn to recognize spoken words (Waibel 1989; Lee 1989), predict recovery rates of pneumonia patients (Cooper et al 1997), detect fraudulent use of credit cards, drive autonomous vehicles on public highways (Pomerleau 1989), and play games such as backgammon at levels approaching the performance of human world champions (Tesauro 1992, 1995) Theoretical results have been developed that characterize the fundamental relationship among the number of training examples observed, the number of hypotheses under consideration, and the expected error in learned hypotheses We are beginning to obtain initial models of human and animal learning and to understand their relationship to learning algorithms developed for computers (eg, Laird et al 1986; Anderson 1991; Qin et al 1992; Chi and Bassock 1989; Ahn and Brewer 1993) In applications, algorithms, theory, and studies of biological systems, the rate of progress has increased significantly over the past decade Several recent applications of machine learning are summarized in Table 11 Langley and Simon (1995) and Rumelhart et al (1994) survey additional applications of machine learning This book presents the field of machine learning, describing a variety of learning paradigms, algorithms, theoretical results, and applications Machine learning is inherently a multidisciplinary field It draws on results from artificial intelligence, probability and statistics, computational complexity theory, control theory, information theory, philosophy, psychology, neurobiology, and other fields Table 12 summarizes key ideas from each of these fields that impact the field of machine learning While the material in this book is based on results from many diverse fields, the reader need not be an expert in any of them Key ideas are presented from these fields using a nonspecialist's vocabulary, with unfamiliar terms and concepts introduced as the need arises
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11 WELL-POSED LEARNING PROBLEMS
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Let us begin our study of machine learning by considering a few learning tasks For the purposes of this book we will define learning broadly, to include any computer program that improves its performance at some task through experience Put more precisely,
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Definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E
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For example, a computer program that learns to play checkers might improve its performance as measured by its abiliry to win at the class of tasks involving playing checkers games, through experience obtained by playing games against itself In general, to have a well-defined learning problem, we must identity these
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