Pattern Classifiers and Trainable Machines

The research program, part of the UCI Pattern Recognition Project, was concerned with the design of trainable classifiers; the graduate courses were broader in scope, including subjects such as feature selection, cluster analysis, ...

Pattern Classifiers and Trainable Machines

Pattern Classifiers and Trainable Machines

This book is the outgrowth of both a research program and a graduate course at the University of California, Irvine (UCI) since 1966, as well as a graduate course at the California State Polytechnic University, Pomona (Cal Poly Pomona). The research program, part of the UCI Pattern Recogni tion Project, was concerned with the design of trainable classifiers; the graduate courses were broader in scope, including subjects such as feature selection, cluster analysis, choice of data set, and estimates of probability densities. In the interest of minimizing overlap with other books on pattern recogni tion or classifier theory, we have selected a few topics of special interest for this book, and treated them in some depth. Some of this material has not been previously published. The book is intended for use as a guide to the designer of pattern classifiers, or as a text in a graduate course in an engi neering or computer science curriculum. Although this book is directed primarily to engineers and computer scientists, it may also be of interest to psychologists, biologists, medical scientists, and social scientists.

Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition

Error-Correcting Codes For Multiclass Recognition, International Journal of Pattern Recognition and Artificial Intelligence 19(5): 663 - 680, 2005. Sklansky, J. and Wassel, G. N., Pattern classifiers and trainable machines.

Ensemble Learning  Pattern Classification Using Ensemble Methods  Second Edition

Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

Pattern Classification Using Ensemble Methods

Sklansky, J. and Wassel, G. N., Pattern classifiers and trainable machines. SpringerVerlag, New York, 1981. Skurichina M. and Duin R.P.W., Bagging, boosting and the random subspace method for linear classifiers.

Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.

A Probabilistic Theory of Pattern Recognition

Sklansky, J. and Michelotti (1980). Locally trained piecewise linear classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:101–111. Sklansky, J. and Wassel, G. (1979). Pattern Classifiers and Trainable Machines.

A Probabilistic Theory of Pattern Recognition

A Probabilistic Theory of Pattern Recognition

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

Fundamentals of Digital Image Processing

“ Fuzzy min - max neural network - Part 1 : classification . ” IEEE Trans . Neural Network , vol . 3 ( 5 ) : 776–785 . SKLANSKY , J. AND G.N. WASSEL ( 1981 ) . Pattern Classifiers and Trainable Machines . New York : Springer - Verlag ...

Fundamentals of Digital Image Processing

Fundamentals of Digital Image Processing


Advances in Logic Artificial Intelligence and Robotics

[ 9 ] J. Rissanen , A Universal Prior for Integers and Estimation by Minimum Description Length , Annals of Statistics 11 ( 1983 ) 416-431 . [ 10 ] J. Sklansky and G. N. Wassel , Pattern Classifiers and Trainable Machines , Springer ...

Advances in Logic  Artificial Intelligence  and Robotics

Advances in Logic Artificial Intelligence and Robotics


Studies in Pattern Recognition

... Pattern Classifiers and Trainable Machines , SpringerVerlag , New York , 1981 . 25. R. Bajcsy and M. Tavakoli , “ A computer recognition of bridges , islands , rivers , and lakes from satellite pictures ” , Proc . of Machine ...

Studies in Pattern Recognition

Studies in Pattern Recognition

More than ten years have passed since the untimely death of King-Sun Fu, one of the great pioneers in the field of pattern recognition. It was he, more than any other single individual, who nurtured the field during its formative years, and set the tone and tempo for others to follow. This book is dedicated to his memory. This book contains 11 chapters by authors who knew King-Sun Fu and in varying degrees interacted with him. The articles span the field of pattern recognition in its current state, and cover such diverse topics as neural nets, covariance propagation, genetic selection, shape description, characteristic views for 3D modeling, face recognition, speech recognition, and machine translation. In tone they vary from the highly theoretical to the applied. Their presentation here is a testimonial, by his former colleagues and friends, to the pioneer who did so much to bring pattern recognition to its position as a recognized discipline world-wide.

Studies in Pattern Recognition

J. Sklansky and G. N. Wassel, Pattern Classifiers and Trainable Machines, SpringerVerlag, New York, 1981. R. Bajcsy and M. Tavakoli, “A computer recognition of bridges, islands, rivers, and lakes from satellite pictures”, ...

Studies in Pattern Recognition

Studies in Pattern Recognition

More than ten years have passed since the untimely death of King-Sun Fu, one of the great pioneers in the field of pattern recognition. It was he, more than any other single individual, who nurtured the field during its formative years, and set the tone and tempo for others to follow. This book is dedicated to his memory. This book contains 11 chapters by authors who knew King-Sun Fu and in varying degrees interacted with him. The articles span the field of pattern recognition in its current state, and cover such diverse topics as neural nets, covariance propagation, genetic selection, shape description, characteristic views for 3D modeling, face recognition, speech recognition, and machine translation. In tone they vary from the highly theoretical to the applied. Their presentation here is a testimonial, by his former colleagues and friends, to the pioneer who did so much to bring pattern recognition to its position as a recognized discipline world-wide. Contents:Pattern Category Assignment by Neural Networks and Nearest Neighbors Rule: A Synopsis and a Characterization (A Mitiche & J K Aggarwal)Pattern Recognition: An Approach to Turn Machine Translation Concepts into Creation and Reality (J T Tou)Learning in Navigation: Goal Finding in Graphs (P Cucka et al.)Subset Least Squares Method for Robust Speech and Image Processing (R L Kashyap & J-N Liaw)Shape Recognition by Human-Like Trial and Error Random Processes (M Nagao)3-D Face Modeling and Its Applications (T S Huang & L-A Tang)Dimension Reduction, Feature Extraction and Interpretation of Data with Network Computing (Y-H Pao)Characteristic-View Modeling of Curved-Surface Solids (S Chen & H Freeman)Propagating Covariance in Computer Vision (R M Haralick)Shape Description by a Syntactic Pyramidal Approach (S Levialdi & L Cinque)Genetic Selection and Neural Modeling of Piecewise Linear-Classifiers (J Sklansky & M Vriesenga) Readership: Computer scientists. keywords:

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