Neural Network Learning

Neural Network Learning This important work describes recent theoretical advances in the study of artificial neural networks It explores probabilistic models of supervised learning problems and addresses the key statistical

  • Title: Neural Network Learning
  • Author: Martin Anthony Peter L. Bartlett
  • ISBN: 9780521118620
  • Page: 133
  • Format: Paperback
  • This important work describes recent 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 Chapters survey research on pattern classification with binary output networks, including a discussion of the relevance of the Vapnik ChervonenkThis important work describes recent 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 Chapters survey research on pattern classification with binary output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models In addition, Anthony and Bartlett develop a model of classification by real output networks, and demonstrate the usefulness of classification with a large margin The authors explain the role of scale sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction Key chapters 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 accessible to researchers and graduate students in computer science, engineering, and mathematics.

    One thought on “Neural Network Learning”

    Leave a Reply

    Your email address will not be published. Required fields are marked *