Bioinformatics: The machine learning approach
by P. Baldi and S. Brunak, MIT Press February 1998.
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
by R. Durbin, S.R. Eddy, A. Krogh, G.J. Mitchison. Focus is mainly on machine learning methods for alignment, phylogeny, and RNA structure analysis.
Computational Methods in Molecular Biology
Edited by S. Salzberg, D. Searls, and S. Kasif. Elsevier Science, 1998. The book is largely devoted to machine learning approaches to molecular biology. The site includes an online appendix.
Introduction to Machine Learning
By Nils J. Nilsson (downloadable draft)
Machine Learning, Neural and Statistical Classification
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
Machine Learning textbook
A textbook by Tom Mitchell, McGraw Hill, 1997.
Reinforcement Learning: An Introduction
by Sutton & Barto, MIT Press, 1998.
Support Vector Machines, Neural Networks and Fuzzy Logic Models
A textbook that provides a thorough, comprehensive and unified introduction to the field of learning from experimental data and soft computing.
Bioinformatics: The machine learning approach
by P. Baldi and S. Brunak, MIT Press February 1998.
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
by R. Durbin, S.R. Eddy, A. Krogh, G.J. Mitchison. Focus is mainly on machine learning methods for alignment, phylogeny, and RNA structure analysis.
Computational Methods in Molecular Biology
Edited by S. Salzberg, D. Searls, and S. Kasif. Elsevier Science, 1998. The book is largely devoted to machine learning approaches to molecular biology. The site includes an online appendix.
Introduction to Machine Learning
By Nils J. Nilsson (downloadable draft)
Machine Learning, Neural and Statistical Classification
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
Machine Learning textbook
A textbook by Tom Mitchell, McGraw Hill, 1997.
Reinforcement Learning: An Introduction
by Sutton & Barto, MIT Press, 1998.
Support Vector Machines, Neural Networks and Fuzzy Logic Models
A textbook that provides a thorough, comprehensive and unified introduction to the field of learning from experimental data and soft computing.
Bioinformatics: The machine learning approach
by P. Baldi and S. Brunak, MIT Press February 1998.
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
by R. Durbin, S.R. Eddy, A. Krogh, G.J. Mitchison. Focus is mainly on machine learning methods for alignment, phylogeny, and RNA structure analysis.
Computational Methods in Molecular Biology
Edited by S. Salzberg, D. Searls, and S. Kasif. Elsevier Science, 1998. The book is largely devoted to machine learning approaches to molecular biology. The site includes an online appendix.
Introduction to Machine Learning
By Nils J. Nilsson (downloadable draft)
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