Introduction to Mathematical Techniques in Pattern Recognition by Harry C. Andrews This volume is one of the first cohesive treatments of the use of mathematics for studying interactions between various recognition environments. It brings together techniques previously scattered throughout the literature and provides a concise common notation that will facilitate the understanding and comparison of the many aspects of mathematical pattern recognition. The contents of this volume are divided into five interrelated subject areas: Feature Selection, Distribution Free Classification, Statistical Classification, Nonsupervised Learning, and Sequential Learning. Appendices describing specific aspects of feature selection and extensive reference and bibliographies are included. 1972 253 pp. Threshold Logic and its Applications by Saburo Muroga This is the first in-depth exposition of threshold logic and its applications using linear programming and integer programming as optimization tools. It presents threshold logic as a unified theory of conventional simple gates, threshold gates and their networks. This unified viewpoint explicitly reveals many important properties that were formerly concealed in the framework of conventional switching theory (based essentially on and, or and not gates). 1971 478 pp. Knowing and Guessing A Quantitative Study of Inference and Information By Satosi Watanabe This volume presents a coherent theoretical view of a field now split into different disciplines: philosophy, information science, cybernetics, psychology, electrical engineering, and physics. The target of investigation is the cognitive process of knowing and guessing. In contrast to traditional philosophy, the approach is quantitative rather than qualitative. The study is formal in the sense that the author is not interested in the contents of knowledge or the physiological mechanism of the process of knowing. "The author?s style is lucid, his comments are illuminating. The result is a fascinating book, which will be of interest to scientists in many different fields." ? Nature 1969 592 pp.From the PublisherProvides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.From the Inside FlapPattern Classification and Scene Analysis By Richard O. Duda and Peter E. Hart Here is a unified, Comprehensive, and up-to-date treatment of the theoretical principles of pattern recognition. These principles are applicable to a great variety of problems of current interest, such as character recognition, speech recognition, speaker identification, fingerprint recognition, the analysis of biomedical photographs, aerial photoreconnaissance, automatic inspection for industrial quality control, and visual systems for robots. Throughout Pattern Classification and Scene Analysis, the authors have balanced their presentation to reflect the relative importance of the many theoretical topics in the field. Pattern Classification and Scene Analysis is the first book to provide comprehensive coverage of both statistical classification theory and computer analysis of pictures. Part I covers Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, and clustering. Part II describes many techniques of current interest in automatic scene analysis, including preprocessing of pictorial data, spatial filtering, shape-description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene anal