Machine Learning Resources: Books

Suggestions welcome

Book Reviews

• Artificial Intelligence/Machine Learning (TechBookReport)

Books

• Analytic Learning
  • Explanation-Based Neural Network Learning: A Lifelong Learning Approach (Sebastian Thrun)
• Artificial Intelligence
  • Proceedings: National Conferences on Artificial Intelligence (MIT Press)
• Automated Reasoning
  • Handbook of Automated Reasoning (Editors: J. Alan Robinson & Andrei Voronkov; 2001)
• Biographies
  • Alan Turing (by Andrew Hodges)
• Classifiers
  • Construction and Assessment of Classification Rules (David J. Hand, John Wiley and Sons, 1997, ISBN 0-471-96583-9)
• Cognitive Science
  • Duality of the Mind (R. Sun, 2003)
  • What is Thought? (E. Baum, 2004; MIT Press)
• Computational Biology
  • Computational Methods in Molecular Biology (Eds.: Salzberg, Searls, & Kasif)
  • Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Candida Ferreira, 2002)
• Data Mining
  • Advances in Knowledge Discovery and Data Mining (Eds.: Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy)
  • Context Mediation among Knowledge Discovery Components (Alex Büchner, Universal Publishers) (9/04)
  • Data Preparation for Data Mining (Dorian Pyle, 3/99)
  • Data Mining (P. Adriaans & D. Zantinge)
  • Perner, P. (2002). Advances in Data Mining: Applications in E-Commerce, Medicine, and Knowledge Management. Berlin: Springer Verlag.
  • Data Mining Methods for Knowledge Discovery (Cios, Pedrycz, & Swiniarski, 1998)
  • Data Mining Techniques for Marketing, Sales and Customer Support (Berry & Linoff)
  • Advanced Methods for Knowledge Discovery from Complex Data Series: Advanced Information and Knowledge Processing (Bandyopadhyay, Maulik, Holder, & Cook (Eds.))
  • Decision Support using Data Mining (Anand and Buchner)
  • Exploratory Analysis and Data Modeling in Functional Neuroimaging (Editors: Friedrich T. Sommer & Andrzej Wichert, 1997)
  • Feature Selection for Knowledge Discovery and Data Mining (Liu and Motoda)
  • Feature Extraction, Construction and Selection: A Data Mining Perpective (Eds: Motoda and Liu)
  • Intelligent Agents for Data Mining and Information Retrieval (Mohammadian, March 2004)
  • Knowledge Acquisition from Databases (Xindong Wu)
  • Machine Learning and Data Mining: Methods and Applications (Michalski, Bratko, and Kubat, 1998; John Wiley & Sons)
  • Mining Very Large Databases with Parallel Processing (Alex Freitas, Simon Lavington)
  • Pattern Recognition Algorithms for Data Mining (Pal & Mitra; Chapman Hall/CRC Press) (9/04)
  • Predictive Data-Mining: A Practical Guide (Weiss & Indurkhya)
  • Principles of Data Mining (David J. Hand, Heikki Mannila, & Padhraic Smyth; 2001)
  • Mining Very Large Databases with Parallel Processing (Freitas & Lavington)
  • Dzeroski, S., & Lavrac, N. (2001). Relational data mining. Berlin: Springer.
  • Rough Sets and Data Mining: Analysis of Imprecise Data (Eds: Lin and Cercone; Kluwer)
  • Seven Methods for Transforming Corporate Data into Business Intelligence (Vasant Dhar and Roger Stein; Prentice-Hall, 1997)
• Ecology
  • Machine Learning Methods for Ecological Applications (Ed.: Alan H. Fielding)
• Evolutionary Computation
  • Adaptive Individuals in Evolving Populations: Models and Algorithms (R. Belew & M. Mitchell)
  • Advances in the Evolutionary Synthesis of Intelligent Agents (Eds. M. Patel, V. Honavar, & K. Balakrishnan+)
  • Evoluationary Algorithms for Single and Multicriteria Design Optimization (A. Osyczka; February 2002)
• Finance
  • Decision Technologies for Financial Engineering: Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (Eds.: Weigand, Abu-Mostafa, and Refenes)
• Formal Analyses
  • An Introduction to Kolmogorov Complexity and its Applications (Li & Vitanyi, Springer-Verlag)
  • Advances in Learning Theory: Methods, Models and Applications (Sykens et al., 2003)
• General Collections (edited)
  • Readings in Machine Learning (Eds: Jude Shavlik and Tom Dietterich)
  • Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems (Eds: Bruce Buchanan & David C. Wilkins)
• Genetic Algorithms
  • Genetic Programming --- An Introduction On the Automatic Evolution of Computer Programs and Its Applications (Banzhaf, Nordin, Keller, & Francone: Morgan Kaufmann)
• Goal-Driven Learning
  • Goal-Driven Learning (Eds: Ashwin Ram & David Leake)
• Graphical Models
  • Graphical Models for Machine Learning and Digital Communication (Brendan J. Frey, August 1998)
  • The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology (Clark Glymour; MIT Press; 2001)
• Image Interpretation
  • Machine Learning and Image Interpretation (Terry Caelli & Walter F. Bischof, Curtin U.; Plenum Press)
• Inductive logic programming
  • Cussens, J. & Dzeroski, S. (Eds.) (2000). Learning language in logic. Berlin: Springer.
  • Dzeroski, S., & Lavrac, N. (2001). Relational data mining. Berlin: Springer.
  • Lavrac, N. & Dzeroski, S. (1994). Inductive logic programming: Techniques and applications. New York: Ellis Horwood.
• Intelligent Systems
  • Design and Application of Intelligent Hybrid Systems
  (Eds.: Abraham, Koppen, & Frank)
• Kernel Methods
  • Kernel Methods for Pattern Analysis (John Shawe-Taylor & Nello Cristianini; 12/03)
  • An Introduction to Support Vector Machines (and other kernel-based learning methods) (N. Cristianini & J. Shawe-Taylor; Cambridge University Press; 1999)
  • Learning Kernel Classifiers: Theory and Algorithms (Ralf Herbrich; MIT Press; 2002)
  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Bernhard Scholkopf and Alexander J. Smola; MIT Press; 2001)
  • Least Squares Support Vector Machines (Suykens, Van Gestel, De Brabanter, De Moor, & Vandewalle; World Scientific Publishers, 2002)
• Knowledge Management
  • Experience Management: Foundations, Development Methodology, and Internet-Based Applications (Ralph Bergmann; 2002)
  • Knowledge Asset Management: Beyond the Process-centred and Product-centred Approaches (Mentzas, Apostolou, Abecker, & Young; 2003)
  • Knowledge Management and Organizational Memories (edited by Rose Dieng-Kuntz and Nada Matta; 2002)
• Learning Theory
  • Computational Learning Theory and Natural Learning Systems Volume IV: Making Learning Systems Practical (Eds.: Greiner, Petsche, & Hanson)
  • An Introduction to Support Vector Machines (and other kernel-based learning methods) (N. Cristianini & J. Shawe-Taylor; Cambridge University Press; 1999)
  • Systems that Learn: An Introduction to Learning Theory (Jain, Osherson, Royer, and Sharma; 3/99)
• Medicine
  • Bioinformatics: The Machine Learning Approach (Baldi and Brunak, 1998)
• Multi-Agent Learning
  • Adaptation and Learning in Multi-Agent Systems (Eds: Weiss and Sen)
  • Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies (Gheorghe Tecuci, 1998)
  • Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet (Matthias Klusch, Ed.; 1999)
• Multi-Strategy Learning
  • Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies (G. Tecuci; 1998)
• Neural Networks
  • Advances in Neural Information Processing Systems Proceedings of the First 12 Conferences (1988-1999) (Eds: Jordan, LeCun, and Solla; 2002)
  • Advances in Neural Information Processing Systems 13 (Eds: Todd K. Leen, Thomas G. Dietterich, & Volker Tresp)
  • Advances in Neural Information Processing Systems 14 (NIPS'01) (Eds: Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani)
  • Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (Nikola Kasabov; MIT Press)
• Planning
  • Machine Learning Methods for Planning (Ed: Steve Minton)
  • Automated Planning: Theory and Practice (5/04: Malik Ghallab, Dana Nau, and Paolo Traverso)
• Power Systems
  • Automatic Learning Techniques in Power Systems (Louis A. Wehenkel)
• Probabilistic Reasoning
  • Learning in Graphical Models (Michael I. Jordan, Editor; 1999)
• Reinforcement Learning
  • Reinforcement Learning: An Introduction (Sutton and Barto)
• Robotics
  • Recent Advances in Robot Learning (Ed. Judy Franklin)
  • Robot Shaping: An Experiment in Behavior Engineering (Dorigo & Colombetti)
• Textbooks
  • A Compendium of Machine Learning, Volume 1: Symbolic Machine Learning (Garry Briscoe & Terry Caelli; Ablex Publishing Corporation)
  • Elements of Machine Learning (Pat Langley)
  • Introduction to Machine Learning (Nils Nilsson; draft)
  • Introduction to Machine Learning (Ethem Alpaydin, 2004)
  • Machine Learning (Tom Mitchell)
• Uncertainty
  • Reasoning About Uncertainty (Joseph Y. Halpern)