Mohlo by sa vám tiež páčiť

Viac informácií o knihe Neural Network Learning

• Parametre knihy
• Anotácia kniha
• Obľúbené z iného súdka

Nákupom získate 197 bodov

Anotácia knihy

First published in 1999, 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.

Parametre knihy

Zaradenie knihy Knihy po anglicky Computing & information technology Computer science Artificial intelligence

• Celý názov: Neural Network Learning
• Podnázov: Theoretical Foundations
• Autor: Martin AnthonyPeter L. Bartlett
• Jazyk: Angličtina
• Väzba: Brožovaná
• Počet strán: 404
• EAN: 9780521118620
• ISBN: 052111862X
• ID: 02022704
• Nakladateľ: Cambridge University Press
• Hmotnosť: 622 g
• Rozmery: 230 × 153 × 24 mm
• Dátum vydania: 20. August 2009

Obľúbené z iného súdka

---

---

---