This comprehensive encyclopedia will provide easy access to relevant information on all aspects of Machine Learning. An A-Z format of several hundred entries will offer an accessible reference for those seeking entry into any aspect within the broad field of Machine Learning. Most entries in this preeminent work will include useful literature references providing the reader with a portal to more detailed information on any given topic. Each entry will include: a definition, key words, an illustration, applications, a bibliography, and links to related literature. Topics for the encyclopedia will be selected by a distinguished international advisory board. Proposed subject areas will include: Classification, Regression, Recognition, and Prediction; Problem Solving and Planning; Reasoning and Inference; Data Mining; Web Mining; Scientific Discovery; Information Retrieval; Natural Language Processing; Design and Diagnosis; Vision and Speech Perception; Robotics and Control; Combinatorial Optimization; Game Playing; Industrial, Financial, and Scientific Applications of All Kinds; Supervised and Unsupervised Learning Methods; Learning Decision and Regression Trees; Rules- Connectionist Networks; Probabilistic Networks and Other Statistical Models; Inductive Logic Programming - Case-Based Methods - Ensemble Methods; Clustering; Reinforcement Learning; Evolution-Based Methods; Explanation-Based Learning; Analogical Learning Methods; Automated Knowledge Acquisition; Learning from Instruction; Visualization of Patterns in Data-Learning in Integrated Architectures; Multi-strategy Learning; and Multi-Agent Learning. The style of the entries in the "Encyclopedia of Machine Learning" is expository and tutorial, making the book a practical resource for high-performance computing experts as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.