Pattern Recognition with Neural Networks in C

The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks.

Author: Abhijit S. Pandya

Publisher: CRC Press

ISBN: 0367448874

Category:

Page: 432

View: 665

Download →

The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks.
Posted in:

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: 644

Download →

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.
Posted in:

Pattern Recognition and Image Analysis

M. Esposito, C. Mazzariello, F. Oliviero, S.P. Romano, C. Sansone, “Real Time Detection of Novel Attacks by Means ... S. C. Lee, D.V. Heinbuch, “Training a neural Network based intrusion detector to recognize novel attack”, IEEE Trans.

Author: Sameer Singh

Publisher: Springer

ISBN: 9783540319993

Category: Computers

Page: 814

View: 860

Download →

This LNCS volume contains the papers presented at the 3rd International Conference on Advances in Pattern Recognition (ICAPR 2005) organized in August, 2005 in the beautiful city of Bath, UK.
Posted in:

Advances in Pattern Recognition ICAPR2003

6 ww d Figure 5 : Testing cases : ( a ) A data case with significant AP ( b ) Edited using the neural network ( c ) Edited using REC . Note that some very high - reflectivity AP values remain . ( d ) Typical spring precipitation ( e ) ...

Author:

Publisher: Allied Publishers

ISBN: 8177645323

Category: Pattern recognition systems

Page: 528

View: 508

Download →

Posted in:

Neural Networks for Pattern Recognition

Christopher M. Bishop, Professor of Neural Computing Christopher M Bishop Oxford University Press. used simply as a non-linear discriminant (Richard and Lippmann, 1991). These include: Minimum error-rate decisions From the discussion of ...

Author: Christopher M. Bishop

Publisher: Oxford University Press

ISBN: 9780198538646

Category: Computers

Page: 501

View: 401

Download →

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.
Posted in:

Cross Disciplinary Applications of Artificial Intelligence and Pattern Recognition Advancing Technologies

Lin, Y. P., Wang, C.-H., Jung, T.-P., Wu, T. L., Jeng, S. K., Duann, J .-R., & Chen, J . H. (2010). ... Image segmentation by using discrete Tchebichef moments and quantum neural network. ... ICA color space for pattern recognition.

Author: Mago, Vijay Kumar

Publisher: IGI Global

ISBN: 9781613504307

Category: Computers

Page: 786

View: 648

Download →

The need for intelligent machines in areas such as medical diagnostics, biometric security systems, and image processing motivates researchers to develop and explore new techniques, algorithms, and applications in this evolving field.Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems. Through its discussions of advances in and applications of pattern recognition technologies and artificial intelligence, this reference highlights core concepts in biometric imagery, feature recognition, and other related fields, along with their applicability.
Posted in:

Applied Pattern Recognition

Pattern recognition by labeled graph matching. Neural Networks, 1:141–148, 1988 39. C. von der Malsburg. The dynamic link architecture. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, 2nd edn., pages 1002– 1005.

Author: Horst Bunke

Publisher: Springer Science & Business Media

ISBN: 9783540768302

Category: Mathematics

Page: 251

View: 706

Download →

A sharp increase in the computing power of modern computers has triggered the development of powerful algorithms that can analyze complex patterns in large amounts of data within a short time period. Consequently, it has become possible to apply pattern recognition techniques to new tasks. The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
Posted in:

Pattern Recognition

In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009) 4. Douarre, C., Crispim-Junior, C.F., ... Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks.

Author: Christian Bauckhage

Publisher: Springer Nature

ISBN: 9783030926595

Category: Computers

Page: 726

View: 844

Download →

This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.
Posted in:

Granular Neural Networks Pattern Recognition and Bioinformatics

Intuitively, for handling data with overlapping patterns (like microarrays), any rough fuzzy clustering (RFC) method, like GSOM, will perform better than SOM, c-medoids, AP method, fuzzy clustering (e.g., fuzzy c-means) and rough ...

Author: Sankar K. Pal

Publisher: Springer

ISBN: 9783319571157

Category: Technology & Engineering

Page: 227

View: 250

Download →

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.
Posted in:

Pattern Recognition in Bioinformatics

IEEE Transactions on Neural Networks 9(5), 768–786 (1998) Bernazzani, L., Duce, C., Micheli, A., Mollica, V., Sperduti, A., Starita, A., Tiné, M.: Predicting physical-chemical properties of compounds from molecular structures by ...

Author: Visakan Kadirkamanathan

Publisher: Springer

ISBN: 9783642040313

Category: Science

Page: 452

View: 193

Download →

This book constitutes the refereed proceedings of the Fourth International Workshop on Pattern Recognition in Bioinformatics, PRIB 2009, held in Sheffield, UK, in September 2009. The 38 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics covered by these papers range from image analysis for biomedical data to systems biology. The conference aims at crating a focus for the development and application of pattern recognition techniques in the biological domain.
Posted in: