Neural Network Learning : Theoretical Foundations / by M. Anthony and P. L. Bartlett. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 1999Description: xiv, 389pISBN:- 9780511624216
- 006.32Â An86N
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 006.32 An86N (Browse shelf(Opens below)) | Available (e-Book For Access) | Platform : Cambridge Core | EB0390 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
006.312 M364E Ethics of Data and Analytics : Concepts and Cases | 006.312 T618R R for Data Science | 006.312 W631R R for Data Science : Import, Tidy, Transform, Visualize and Model Data | 006.32 An86N Neural Network Learning : Theoretical Foundations | 006.32 Sh61I Image Processing and Pattern Recognition : Fundamentals and Techniques | 006.33 G167B Belief Revision | 006.33 M576E Epistemic Logic for AI and Computer Science |
This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.
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