Is achievable and what is not this book demonstrates the value of a systematic methodology when used along with the needed techniues for evaluating the performance of a learning system First an introduction to machine learning is presented that includes brief discussions of applications such as image recognition speech recognition medical diagnostics and statistical arbitrage To enhance accessibility two chapters on relevant aspects of probability theory are provided Subseuent chapters feature coverage of topics such as the pattern recognition problem optimal Bayes decision rule the nearest neighbor rule kernel rules neural networks support vector machines and boostingAppendices throughout the book explore the relationship between the discussed materi.

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