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Machine learning

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As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Machine learning

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Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Textbooks. --- Textbooks

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China is the world's largest market for machine- and cutting-tools, so advanced manufacturing technology and cutting tools can be expected to enjoy not only a good market share but also huge opportunities for development and advancement. The present volume contains 28 peer-reviewed papers selected from among the 60 (from academia and industry) presented at the seminar on Advanced Manufacturing Technology and Cutting Tools (SAMTCT2011) which was held during the 20-22th August 2011 in Shanghai (China). This work presents readers with a broad overview of recent advances in the fields of manufactu

Production engineering --- Machine-tools

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What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.

Computational learning theory. --- Machine learning. --- Relational databases. --- Relational data bases --- Databases --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine learning