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CHAPTER 1 INTRODUCITON
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Learning to recognize spoken words All of the most successful speech recognition systems employ machine learning in some form For example, the SPHINX system (eg, Lee 1989) learns speaker-specific strategies for recognizing the primitive sounds (phonemes) and words from the observed speech signal Neural network learning methods (eg, Waibel et al 1989) and methods for learning hidden Markov models (eg, Lee 1989) are effective for automatically customizing to,individual speakers, vocabularies, microphone characteristics, background noise, etc Similar techniques have potential applications in many signal-interpretation problems Learning to drive an autonomous vehicle Machine learning methods have been used to train computer-controlled vehicles to steer correctly when driving on a variety of road types For example, the ALVINN system (Pomerleau 1989) has used its learned strategies to drive unassisted at 70 miles per hour for 90 miles on public highways among other cars Similar techniques have possible applications in many sensor-based control problems Learning to classify new astronomical structures Machine learning methods have been applied to a variety of large databases to learn general regularities implicit in the data For example, decision tree learning algorithms have been used by NASA to learn how to classify celestial objects from the second Palomar Observatory Sky Survey (Fayyad et al 1995) This system is now used to automatically classify all objects in the Sky Survey, which consists of three terrabytes of image data Learning to play world-class backgammon The most successful computer programs for playing games such as backgammon are based on machiie learning algorithms For example, the world's top computer program for backgammon, TD-GAMMON (Tesauro 1992, 1995) learned its strategy by playing over one million practice games against itself It now plays at a level competitive with the human world champion Similar techniques have applications in many practical problems where very large search spaces must be examined efficiently
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TABLE 11 Some successful applications of machiie learning
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three features: the class of tasks, the measure of performance to be improved, and the source of experience
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A checkers learning problem:
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Task T: playing checkers Performance measure P: percent of games won against opponents Training experience E: playing practice games against itself
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We can specify many learning problems in this fashion, such as learning to recognize handwritten words, or learning to drive a robotic automobile autonomously
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A handwriting recognition learning problem:
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Task T: recognizing and classifying handwritten words within images Performance measure P : percent of words correctly classified
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MACHINE LEARNING
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Artificial intelligence Learning symbolic representations of concepts Machine learning as a search problem Learning as an approach to improving problem solving Using prior knowledge together with training data to guide learning Bayesian methods Bayes' theorem as the basis for calculating probabilities of hypotheses The naive Bayes classifier Algorithms for estimating values of unobserved variables Computational complexity theory Theoretical bounds on the inherent complexity of different learning tasks, measured in terms of the computational effort, number of training examples, number of mistakes, etc required in order to learn Control theory Procedures that learn to control processes in order to optimize predefined objectives and that learn to predict the next state of the process they are controlling Information theory Measures of entropy and information content Minimum description length approaches to learning Optimal codes and their relationship to optimal training sequences for encoding a hypothesis Philosophy Occam's razor, suggesting that the simplest hypothesis is the best Analysis of the justification for generalizing beyond observed data Psychology and neurobiology The power law of practice, which states that over a very broad range of learning problems, people's response time improves with practice according to a power law Neurobiological studies motivating artificial neural network models of learning Statistics Characterization of errors (eg, bias and variance) that occur when estimating the accuracy of a hypothesis based on a limited sample of data Confidence intervals, statistical tests
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