Author : Sergey V. Ablameyko
Publisher : Springer Nature
Page : 320 pages
File Size : 24,44 MB
Release : 2019-11-22
Category : Computers
ISBN : 303035430X
This book constitutes the refereed proceedings of the 14th International Conference on Pattern Recognition and Information Processing, PRIP 2019, held in Minsk, Belarus, in May 2019. The 25 revised full papers were carefully reviewed and selected from 120 submissions. The papers of this volume are organized in topical sections on pattern recognition and image analysis; information processing and applications.
Author : Mitra Basu
Publisher : Springer Science & Business Media
Page : 309 pages
File Size : 31,19 MB
Release : 2006-12-22
Category : Computers
ISBN : 1846281725
Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
Author :
Publisher :
Page : 511 pages
File Size : 41,60 MB
Release : 2005
Category :
ISBN : 9789856329558
Author : Viktor V. Krasnoproshin
Publisher : Springer
Page : 187 pages
File Size : 16,38 MB
Release : 2017-02-15
Category : Computers
ISBN : 3319542206
This book constitutes the refereed proceedings of the 13th International Conference on Pattern Recognition and Information Processing, PRIP 2016, held in Minsk, Belarus, in October 2016. The 18 revised full papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on summarizing lectures; pattern recognition and image analysis; information processing and applications.
Author : Stephen K. Reed
Publisher : Academic Press
Page : 261 pages
File Size : 16,76 MB
Release : 2013-09-11
Category : Psychology
ISBN : 1483263347
Psychological Processes in Pattern Recognition describes information-processing models of pattern recognition. This book is organized into five parts encompassing 11 chapters that particularly focus on visual pattern recognition and the many issues relevant to a more general theory of pattern recognition. The first three parts cover the representation, temporal effects, and memory codes of pattern recognition. These parts include the features, templates, schemata, and structural descriptions of information processing models. The principles of parallel matching, iconic storage, and the components and networks of memory codes are also considered. The remaining two parts look into the perceptual classification and response selection of pattern recognition. These parts specifically tackle the development of probability, distance, and recognition models. This book is intended primarily for psychologists, graduate students, and researchers who are interested in the problems of pattern recognition and human information processing.
Author : Alexander V. Tuzikov
Publisher : Springer
Page : 245 pages
File Size : 48,31 MB
Release : 2022-03-18
Category : Computers
ISBN : 9783030988821
This book constitutes the refereed proceedings of the 15th International Conference on Pattern Recognition and Information Processing, PRIP 2021, held in Minsk, Belarus, in September 2021. Due to the COVID-19 pandemic the conference was held online. The 17 revised full papers were carefully reviewed and selected from 90 submissions. The papers present a discussion on theoretical and applied aspects of computer vision, recognition of signals and images, the use of distributed resources, and high-performance systems.
Author : Wu Chou
Publisher : CRC Press
Page : 413 pages
File Size : 13,95 MB
Release : 2003-02-26
Category : Technology & Engineering
ISBN : 0203010523
Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco
Author : Christopher M. Bishop
Publisher : Springer
Page : 0 pages
File Size : 42,31 MB
Release : 2016-08-23
Category : Computers
ISBN : 9781493938438
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author : Yoh-Han Pao
Publisher : Addison Wesley Publishing Company
Page : 344 pages
File Size : 39,33 MB
Release : 1989
Category : Computers
ISBN :
A coherent introduction to the basic concepts of pattern recognition, incorporating recent advances from AI, neurobiology, engineering, and other disciplines. Treats specifically the implementation of adaptive pattern recognition to neural networks. Annotation copyright Book News, Inc. Portland, Or.
Author : Francisco Escolano Ruiz
Publisher : Springer Science & Business Media
Page : 375 pages
File Size : 49,66 MB
Release : 2009-07-14
Category : Computers
ISBN : 1848822979
Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.
