Pattern Recognition: Architectures, Algorithms And Applications

Pattern Recognition: Architectures, Algorithms And Applications
Author: Rejean Plamondon
Publisher: World Scientific
Total Pages: 404
Release: 1991-08-12
Genre: Computers
ISBN: 9814506303

This book contains 15 reviewed papers selected from among those presented at the 4th Vision Interface Conference in Halifax, Canada 14 - 18 May 1990. The papers are grouped into three sections which deal with parallel architectures and neural networks, algorithms for analysis and processing, and systems and applications.

Computational Intelligence for Pattern Recognition

Computational Intelligence for Pattern Recognition
Author: Witold Pedrycz
Publisher: Springer
Total Pages: 431
Release: 2018-04-30
Genre: Technology & Engineering
ISBN: 3319896296

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

Pattern Recognition

Pattern Recognition
Author: R‚jean Plamondon
Publisher: World Scientific
Total Pages: 404
Release: 1991
Genre: Computers
ISBN: 9789810206048

This book contains 15 reviewed papers selected from among those presented at the 4th Vision Interface Conference in Halifax, Canada 14 - 18 May 1990. The papers are grouped into three sections which deal with parallel architectures and neural networks, algorithms for analysis and processing, and systems and applications.

Artificial Neural Networks in Pattern Recognition

Artificial Neural Networks in Pattern Recognition
Author: Luca Pancioni
Publisher: Springer
Total Pages: 415
Release: 2018-08-29
Genre: Computers
ISBN: 3319999788

This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Handbook Of Pattern Recognition And Computer Vision (2nd Edition)

Handbook Of Pattern Recognition And Computer Vision (2nd Edition)
Author: Chi Hau Chen
Publisher: World Scientific
Total Pages: 1045
Release: 1999-03-12
Genre: Computers
ISBN: 9814497649

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Author: Christopher M. Bishop
Publisher: Oxford University Press
Total Pages: 501
Release: 1995-11-23
Genre: Computers
ISBN: 0198538642

Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
Author: Evangelia Miche Tzanakou
Publisher: CRC Press
Total Pages: 475
Release: 2017-12-19
Genre: Technology & Engineering
ISBN: 1351835556

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Pattern Recognition Using Neural Networks

Pattern Recognition Using Neural Networks
Author: Carl G. Looney
Publisher: Oxford University Press on Demand
Total Pages: 458
Release: 1997
Genre: Computers
ISBN: 9780195079203

Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiple-layered perceptrons, functional link nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, self-organizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions.

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Author: James C. Bezdek
Publisher: Springer Science & Business Media
Total Pages: 267
Release: 2013-03-13
Genre: Mathematics
ISBN: 147570450X

The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.