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Pattern Recognition and Artificial Intelligence


Book Description

Pattern Recognition and Artificial Intelligence contains the proceedings of the Joint Workshop on Pattern Recognition and Artificial Intelligence held in Hyannis, Massachusetts, on June 1-3, 1976. The papers explore developments in pattern recognition and artificial intelligence and cover topics ranging from scene analysis and data structure to syntactic methods, biomedicine, speech recognition, game-playing programs, and computer graphics. Grammar inference methods, image segmentation and interpretation, and relational databases are also discussed. This book is comprised of 29 chapters and begins with a description of a data structure that can learn simple programs from training samples. The reader is then introduced to the syntactic parts of pattern recognition systems; methods for multidimensional grammatical inference; a scene analysis system capable of finding structure in outdoor scenes; and a language called DEDUCE for relational databases. A sculptor's studio-like environment, in which the ""sculptor"" can create complex three-dimensional objects in the computer similar to molding a piece of clay in the machine, is also described. The remaining chapters focus on statistical and structural feature extraction; use of maximum likelihood functions for recognition of highly variable line drawings; region extraction using boundary following; and interactive screening of reconnaissance imagery. This monograph will be of interest to engineers, graduate students, and researchers in the fields of pattern recognition and artificial intelligence.




7th Int. Conf. Industrial & En


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Methodologies for Intelligent Systems


Book Description

This volume contains the revised versions of the papers presented at the Eighth International Symposium on Methodologies for Intelligent Systems (ISMIS '94), held in Charlotte, North Carolina, USA in October 1994. Besides four invited contributions by renowned researchers on key topics, there are 56 full papers carefully selected from more than 120 submissions. The book presents the state of the art for methodologies for intelligent systems; the papers are organized in sections on approximate reasoning, evolutionary computation, intelligent information systems, knowledge representation, methodologies, learning and adaptive systems, and logic for AI.




Proceedings


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Machine Learning


Book Description

Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.




Classification and Clustering


Book Description

Classification and Clustering documents the proceedings of the Advanced Seminar on Classification and Clustering held in Madison, Wisconsin on May 3-5, 1976. This compilation discusses the relationship between multidimensional scaling and clustering, distribution problems in clustering, and botryology of botryology. The graph theoretic techniques for cluster analysis algorithms, data dependent clustering techniques, and linguistic approach to pattern recognition are also elaborated. This text likewise covers the discriminant analysis when scale contamination is present in the initial sample and statistical basis of computerized diagnosis using the electrocardiogram. Other topics include the simple histogram method for nonparametric classification and optimal smoothing of density estimates. This book is intended for mathematicians, biological scientists, social scientists, computer scientists, statisticians, and engineers interested in classification and clustering.