Pattern Recognition with Neural Networks in C

Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.

Author: Abhijit S. Pandya

Publisher: CRC Press

ISBN: 0849394627

Category: Computers

Page: 432

View: 173

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The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
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Pattern Recognition with Neural Networks in C

Haykin, S., Neural Networks, A Comprehensive Foundation, IEEE Society Press, Macmillan College Publishing, New York, 1994. Herman, G. T., Odhner, D., and Yeung, K. T. D., “Optimization for pattern classification using biased random ...

Author: Abhijit S. Pandya

Publisher: CRC Press

ISBN: 9780429606212

Category: Computers

Page: 432

View: 459

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The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
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Pattern Recognition and Neural Networks

Carroll , S. M. & Dickinson , B. W. ( 1989 ) Construction of neural nets using the Radon transform . In Proceedings of the International Joint Conference on Neural Networks I , 607–611 . New York : IEEE Press . Carter , C. & Catlett ...

Author: Brian D. Ripley

Publisher: Cambridge University Press

ISBN: 0521717701

Category: Computers

Page: 403

View: 177

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This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.
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Pattern Recognition Using Neural and Functional Networks

Aleksander, I., Morton, H.: An Introduction to Neural Computing. Chapman and Hall, London (1990) 3. Amari, S.: Learning Patterns and Pattern Sequences by Self Organizing Nets of Threshold Elements. IEEE Transactions on Computers C-21, ...

Author: Vasantha Kalyani David

Publisher: Springer Science & Business Media

ISBN: 9783540851295

Category: Mathematics

Page: 184

View: 296

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Biologically inspiredcomputing isdi?erentfromconventionalcomputing.Ithas adi?erentfeel; often the terminology does notsound like it’stalkingabout machines.The activities ofthiscomputingsoundmorehumanthanmechanistic as peoplespeak ofmachines that behave, react, self-organize,learn, generalize, remember andeven to forget.Much ofthistechnology tries to mimic nature’s approach in orderto mimicsome of nature’s capabilities.They havearigorous, mathematical basisand neuralnetworks forexamplehaveastatistically valid set on which the network istrained. Twooutlinesaresuggestedasthepossibletracksforpatternrecognition.They are neuralnetworks andfunctionalnetworks.NeuralNetworks (many interc- nected elements operating in parallel) carryout tasks that are not only beyond the scope ofconventionalprocessing but also cannotbeunderstood in the same terms.Imagingapplicationsfor neuralnetworksseemtobea natural?t.Neural networks loveto do pattern recognition. A new approachto pattern recognition usingmicroARTMAP together with wavelet transforms in the context ofhand written characters,gestures andsignatures havebeen dealt.The KohonenN- work,Back Propagation Networks andCompetitive Hop?eld NeuralNetwork havebeen considered for various applications. Functionalnetworks,beingageneralizedformofNeuralNetworkswherefu- tionsarelearnedratherthanweightsiscomparedwithMultipleRegressionAn- ysisforsome applicationsandtheresults are seen to be coincident. New kinds of intelligence can be added to machines, and we will havethe possibilityof learningmore about learning.Thus our imaginationsand options are beingstretched.These new machines will be fault-tolerant,intelligentand self-programmingthustryingtomakethemachinessmarter.Soastomakethose who use the techniques even smarter. Chapter1 isabrief introduction toNeural and Functionalnetworks in the context of Patternrecognitionusing these disciplinesChapter2 givesa review ofthearchitectures relevantto the investigation andthedevelopment ofthese technologies in the past few decades. Retracted VIII Preface Chapter3begins with the lookattherecognition ofhandwritten alphabets usingthealgorithm for ordered list ofboundary pixelsas well as the Ko- nenSelf-Organizing Map (SOM).Chapter 4 describes the architecture ofthe MicroARTMAP and its capability.
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Neural Networks for Pattern Recognition

Network models in behavioral and computational neuroscience . To appear in Non - animal Models in Biomedical and Psychological Research , Testing , and Education , New Gloucester , PsyETA . James A. Reggia , C. Lynne D'Autrechy ...

Author: Albert Nigrin

Publisher: MIT Press

ISBN: 0262140543

Category: Computers

Page: 413

View: 817

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In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction. Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.
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Neural Networks in Pattern Recognition and Their Applications

Table 2. Number of correct and incorrect classifications on 40 test patterns (20 from each class). C. virginica, C.: versicolor; C/C, means “true class” = C, with “network response” = C, hence i = j implies correct response. Test no.

Author: C H Chen

Publisher: World Scientific

ISBN: 9789814505994

Category: Computers

Page: 168

View: 405

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The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications. Contents:Introduction (C H Chen)Combined Neural-Net/Knowledge-Based Adaptive Systems for Large Scale Dynamic Control (A D C Holden & S C Suddarth)A Connectionist Incremental Expert System Combining Production Systems and Associative Memory (H F Yin & P Liang)Optimal Hidden Units for Two-Layer Nonlinear Feedforward Networks (T D Sanger)An Incremental Fine Adjustment Algorithm for the Design of Optimal Interpolating Networks (S-K Sin & R J P deFigueiredo)On the Asymptotic Properties of Recurrent Neural Networks for Optimization (J Wang)A Real-Time Image Segmentation System Using a Connectionist Classifier Architecture (W E Blanz & S L Gish)Segmentation of Ultrasonic Images with Neural Networks (R H Silverman)Connectionist Model Binarization (N Babaguchi, et al.)An Assessment of Neural Network Technology's on Automatic Active Sonar Classifier Development (T B Haley)On the Relationships between Statistical Pattern Recognition and Artificial Neural Networks (C H Chen) Readership: Computer scientists and engineers. keywords: “The emphasis of this book is genuinely on practical techniques — a rarity in books on neural networks … there is much here that will interest the neural computing specialist.” Neural and Computing Applications
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Pattern Recognition

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing . Kluwer Academic Publishers , Boston , 1999 . [ 8 ] C.M. Bishop . Neural Networks for Pattern Recognition . Clarendon Press , Oxford , 1995 . [ 9 ] I. Bloch .

Author: Sankar K. Pal

Publisher: World Scientific

ISBN: 981238653X

Category: Computers

Page: 612

View: 737

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This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource. Contents: Pattern Recognition: Evolution of Methodologies and Data Mining (A Pal & S K Pal); Adaptive Stochastic Algorithms for Pattern Classification (M A L Thathachar & P S Sastry); Shape in Images (K V Mardia); Decision Trees for Classification: A Review and Some New Results (R Kothari & M Dong); Syntactic Pattern Recognition (A K Majumder & A K Ray); Fuzzy Sets as a Logic Canvas for Pattern Recognition (W Pedrycz & N Pizzi); Neural Network Based Pattern Recognition (V David Sanchez A); Networks of Spiking Neurons in Data Mining (K Cios & D M Sala); Genetic Algorithms, Pattern Classification and Neural Networks Design (S Bandyopadhyay et al.); Rough Sets in Pattern Recognition (A Skowron & R Swiniarski); Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods (E H Ruspini & I S Zwir); Writing Speed and Writing Sequence Invariant On-line Handwriting Recognition (S-H Cha & S N Srihari); Tongue Diagnosis Based on Biometric Pattern Recognition Technology (K Wang et al.); and other papers. Readership: Graduate students, researchers and academics in pattern recognition.
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Artificial Neural Networks in Pattern Recognition

Pattern Recognition, 36(10):2271– 2285, 2003. B. Mel. Seemore: Combining colour, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation, 9:777–804, 1997. C. Nakajima, M. Pontil, ...

Author: Friedhelm Schwenker

Publisher: Springer

ISBN: 9783540379522

Category: Computers

Page: 302

View: 195

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This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.
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Granular Neural Networks Pattern Recognition and Bioinformatics

In pattern recognition framework the diseased microarray data can be viewed as a data with larger number of ... In this chapter, the problem of finding relevant genes is handled with an unsupervised granular neural network using fuzzy ...

Author: Sankar K. Pal

Publisher: Springer

ISBN: 9783319571157

Category: Technology & Engineering

Page: 227

View: 170

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This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
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Neural Networks in Vision and Pattern Recognition

5. C. Roads, “Grammars as representations for music”, Computer Music J. 3, 1 (1979) 48–55. 6. K. S. Fu, “Introduction”, Digital Pattern Recognition, eds. K. S. Fu et al., SpringerVerlag, Berlin, 1980, pp. 1–14. 7.

Author: J Skrzypek

Publisher: World Scientific

ISBN: 9789814505437

Category: Technology & Engineering

Page: 224

View: 624

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The neural network paradigm with its various advantages might be the next promising bridge between artificial intelligence and pattern recognition that will help with the conceptualization of new computational artifacts. This volume contains ten papers which represent some of the work being done in the field, such as in computational neuroscience, pattern recognition, computational vision, and applications. Contents:Introduction (J Skrzypek & W Karplus)Lightness Constancy from Luminance Contrast (J Skrzypek & D Gungner)Bringing the Grandmother Back into the Picture: A Memory-Based View of Object Recognition (S Edelman & T Poggio)Internal Organization of Classifier Networks Trained by Backpropagation (D F Michaels)System Identification with Artificial Neural Networks (E R Tisdale & W J Karplus)Mixed Finite Element Based Neural Networks in Visual Reconstruction (D Suter)The Random Neural Network Model for Texture Generation (V Atalay et al.)Neural Networks for Collective Translational Invariant Object Recognition (L-W Chan)Image Recognition and Reconstruction Using Associative Magnetic Processing (J M Goodwin et al.)Incorporating Uncertainty in Neural Networks (B R Kämmerer)Neural Networks for the Recognition of Engraved Musical Scores (P Martin & C Bellissant) Readership: Computer scientists, engineers and neuroscientists. keywords:
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