Buch, Englisch, 426 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1020 g
Buch, Englisch, 426 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1020 g
ISBN: 978-0-8493-9462-1
Verlag: Routledge
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.
Zielgruppe
Academic and Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
Weitere Infos & Material
1. Introduction 2. Neural Networks: An Overview 3. Preprocessing 4. Feed-Forward Networks with Supervised Learning 5. Some Other Types of Neural Networks 6. Feature Extraction I: Geometric Features and Transformations 7. Feature Extraction II: Principal Component Analysis 8. Kohonen Networks and Learning Vector Quantization 9. Neural Associative Memories and Hopfield Networks 10. Adaptive Resonance Theory (ART) 11. Neocognitron 12. Systems with Multiple Classifiers
