Bøger af Martin Anthony

My name is Martin Anthony. I'm a young man living the dream of being a Metalhead. In 2010 I left home for six weeks to take a solo trip of a lifetime to visit the United States and Canada. For those six weeks, I was on my own, achieving a lifelong goal to visit North America. For six weeks I went across North America to visit eight great cities. In these six weeks I was able to see the sights I had wanted to visit, watch the Hard Rock and Heavy Metal bands I wanted to see and understand what being alone with only $5000 on tour is like. I survived a bomb threat in Times Square, walked through freeways to get to a Winger gig, dodged a jaywalking fine by a NYPD officer, shook hands with Ann and Nancy Wilson, walked the streets and avenues of Manhattan, crawled on my hands and feet to visit Lady Liberty and had a brush with Pornstar Carol Cox in Montreal. This is my Journal.

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.

This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks.