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| Author | : Mendel |
| Publisher | : Academic Press |
| Total Pages | : 461 |
| Release | : 1970-02-28 |
| Genre | : Computers |
| ISBN | : 0080955754 |
Adaptive, Learning, and Pattern Recognition Systems; theory and applications
| Author | : Kumpati S. Narendra |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 410 |
| Release | : 2013-11-22 |
| Genre | : Mathematics |
| ISBN | : 1475718950 |
This volume offers a glimpse of the status of research in adaptive and learning systems in 1985. In recent years these areas have spawned a multiplicity of ideas so rapidly that the average research worker or practicing engineer is overwhelmed by the flood of information. The Yale Workshop on Applications of Adaptive Systems Theory was organized in 1979 to provide a brief respite from this deluge, wherein critical issues may be examined in a calm and collegial environment. The fourth of the series having been held in May 1985, it has now become well established as a biennial forum for the lively exchange of ideas in the ever changing domain of adaptive systems. The scope of this book is broad and ranges from theoretical investigations to practical applications. It includes twenty eight papers by leaders in the field, selected from the Pro ceedings of the Fourth Yale Workshop and divided into five sections. I have provided a brief introduction to each section so that it can be read as a self-contained unit. The first section, devoted to adaptive control theory, suggests the intensity of activity in the field and reveals signs of convergence towards some common themes by workers with rather different moti vation. Preliminary results concerning the reduced order model problem are dramatically changing the way we view the field and bringing it closer to other areas such as robust linear control where major advances have been recently reported.
| Author | : Peter W. Becker |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 229 |
| Release | : 2013-06-29 |
| Genre | : Computers |
| ISBN | : 3709141036 |
The work described in this publication was initiated at the General Electric Company's Electronics Laboratory, Syracuse, N.Y., U.S.A. The author would like to take this opportunity to express his gratitude to the Electronics Laboratory for its support and encouragement in this work. Thanks are in particular due to Mr. J.J. Suran for his continued interest and help. It is impossible to acknowledge all the help the author has re ceived from members of the Laboratory staff. However, the author is par ticularly indebted to Mr. T.C. Robbins for managing the building of the word recognizer (described in Section 7.4) and for many helpful discussions. Thanks are also due to Mr. W.E. Sollecito for valued support and direction, and to S.M. Korzekwa, S.B. Akers, Jr., and B.L. Crew for many discussions on implementation and design of pattern recognizers. Part of the work has been sponsored by two departments of the General Electric Company, the Large Jet Engine Department and the Apollo Support Department. The author is grateful for the permission from the two departments to publish results of theoretical interest in this dissertation. The work was later continued in Denmark, supported by two grants: no.
| Author | : Victor S Kennedy |
| Publisher | : Elsevier |
| Total Pages | : 556 |
| Release | : 2013-10-02 |
| Genre | : Science |
| ISBN | : 1483277496 |
Estuarine Perspectives presents most of the invited papers presented at the Fifth Biennial International Research Conference on Estuarine Research. The book includes information on one tropical and two Arctic estuaries; contemporary techniques as applied to estuarine research; and some hypotheses of estuarine ecology. The text also describes value and management of wetlands as well as the chemical cycles and fluxes. The primary production and photosynthesis; the physical and biological factors of estuarine sediment; and the ecosystem dynamics are also encompassed.
| Author | : Keinosuke Fukunaga |
| Publisher | : Elsevier |
| Total Pages | : 386 |
| Release | : 1972-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0323162789 |
Introduction to Statistical Pattern Recognition introduces the reader to statistical pattern recognition, with emphasis on statistical decision and estimation. Pattern recognition problems are discussed in terms of the eigenvalues and eigenvectors. Comprised of 11 chapters, this book opens with an overview of the formulation of pattern recognition problems. The next chapter is devoted to linear algebra, with particular reference to the properties of random variables and vectors. Hypothesis testing and parameter estimation are then discussed, along with error probability estimation and linear classifiers. The following chapters focus on successive approaches where the classifier is adaptively adjusted each time one sample is observed; feature selection and linear mapping for one distribution and multidistributions; and problems of nonlinear mapping. The final chapter describes a clustering algorithm and considers criteria for both parametric and nonparametric clustering. This monograph will serve as a text for the introductory courses of pattern recognition as well as a reference book for practitioners in the fields of mathematics and statistics.
| Author | : King-Sun Fu |
| Publisher | : CRC Press |
| Total Pages | : 270 |
| Release | : 2018-05-04 |
| Genre | : Technology & Engineering |
| ISBN | : 1351085239 |
It has been recognized for a long time that a conventional sequential processor is inefficient for operations on pictorial data where relatively simple operations need to be performed on a large number of data elements (pixels). Though many parallel processing architectures for picture processing have been proposed in the past, very few have actually been implemented due to the costs involved. With LSI technology, it is becoming possible to realize parallel architectures at a modest cost. In the following the authors review some of the proposed architectures for pattern recognition and image processing.
| Author | : W. Thomas Miller |
| Publisher | : MIT Press |
| Total Pages | : 548 |
| Release | : 1995 |
| Genre | : Computers |
| ISBN | : 9780262631617 |
Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series
| Author | : Andrew R. Webb |
| Publisher | : John Wiley & Sons |
| Total Pages | : 516 |
| Release | : 2003-07-25 |
| Genre | : Mathematics |
| ISBN | : 0470854782 |
Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a
| Author | : Plamen Parvanov Angelov |
| Publisher | : World Scientific |
| Total Pages | : 1057 |
| Release | : 2022-06-29 |
| Genre | : Computers |
| ISBN | : 9811247331 |
The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Related Link(s)
