This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations.
Key Features:
Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
"synopsis" may belong to another edition of this title.
This book looks at both classical and modern methods of data mining, such as clustering, discriminate analysis, decision trees, neural networks and support vector machines along with illustrative examples throughout the book to explain the theory of these models. Recent methods such as bagging and boosting, decision trees, neural networks, support vector machines and genetic algorithm are also discussed along with their advantages and disadvantages.
Key Features:
Business intelligence analysts and statisticians, compliance and financial experts in both commercial and government organizations across all industry sectors will benefit from this book.
Dr Stephane Tuffery teaches Data Mining and statistics, University Rennes 1, Paris, France.
Translator, Rod Riesco, UK.
"About this title" may belong to another edition of this title.
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