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Author: Christopher M. Bishop
Publisher: Oxford University Press
Published: 1995-11-23
Total Pages: 501
ISBN-13: 0198538642
DOWNLOAD EBOOKStatistical 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.
Author: Luca Pancioni
Publisher: Springer
Published: 2018-08-29
Total Pages: 415
ISBN-13: 3319999788
DOWNLOAD EBOOKThis 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.
Author: Brian D. Ripley
Publisher: Cambridge University Press
Published: 2007
Total Pages: 420
ISBN-13: 9780521717700
DOWNLOAD EBOOKThis 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.
Author: I.K. Sethi
Publisher: Elsevier
Published: 2014-06-28
Total Pages: 286
ISBN-13: 148329787X
DOWNLOAD EBOOKWith the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.
Author: Yoh-Han Pao
Publisher: Addison Wesley Publishing Company
Published: 1989
Total Pages: 344
ISBN-13:
DOWNLOAD EBOOKA 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: Robert A. Dunne
Publisher: Wiley-Interscience
Published: 2007-07-16
Total Pages: 296
ISBN-13:
DOWNLOAD EBOOKThis book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.
Author: Abhijit S. Pandya
Publisher: CRC Press
Published: 1995-10-17
Total Pages: 434
ISBN-13: 9780849394621
DOWNLOAD EBOOKThe 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.
Author: Samir Kumar Bandyopadhyay
Publisher: Springer Science & Business Media
Published: 2010-06-09
Total Pages: 330
ISBN-13: 3642133649
DOWNLOAD EBOOKAdvanced Science and Technology, Advanced Communication and Networking, Information Security and Assurance, Ubiquitous Computing and Multimedia Appli- tions are conferences that attract many academic and industry professionals. The goal of these co-located conferences is to bring together researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of advanced science and technology, advanced communication and networking, information security and assurance, ubiquitous computing and m- timedia applications. This co-located event included the following conferences: AST 2010 (The second International Conference on Advanced Science and Technology), ACN 2010 (The second International Conference on Advanced Communication and Networking), ISA 2010 (The 4th International Conference on Information Security and Assurance) and UCMA 2010 (The 2010 International Conference on Ubiquitous Computing and Multimedia Applications). We would like to express our gratitude to all of the authors of submitted papers and to all attendees, for their contributions and participation. We believe in the need for continuing this undertaking in the future. We acknowledge the great effort of all the Chairs and the members of advisory boards and Program Committees of the above-listed events, who selected 15% of over 1,000 submissions, following a rigorous peer-review process. Special thanks go to SERSC (Science & Engineering Research Support soCiety) for supporting these - located conferences.
Author: Gail A. Carpenter
Publisher: MIT Press
Published: 1991
Total Pages: 724
ISBN-13: 9780262031769
DOWNLOAD EBOOKPattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.
Author: Carl G. Looney
Publisher: Oxford University Press on Demand
Published: 1997
Total Pages: 458
ISBN-13: 9780195079203
DOWNLOAD EBOOKPattern 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.
