Neural Network Project Management

Martin Anthony, Peter L. Bartlett, "Neural Network Learning: Theoretical Foundations"
Martin Anthony, Peter L. Bartlett, "Neural Network Learning: Theoretical Foundations"
Cambridge University Press | 3119-19-31 | ISBN: 163333963X | 616 pages | PDF | 9, 6 MB
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 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

Source: jzzovre

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Stock market analysts on trial

2002-02-09 14:24:25 by on-trial

The amount of poor and self-interested advice that is being issued by brokerages and their analysts. To this day, the majority of stockbrokers are compensated on the number of trades their customers make, not on the returns they generate for them or on the quality of the advice they provide. We believe that the price targets and analyst ratings are made with several masters in mind, none of whom are the individual investor. In a similar fashion, sell-side stock analysts are generally compensated based upon the overall profitability of their firms, not the quality or accuracy of their analysis. In the end, analysts have minimal structural incentive to be accurate in their predictions; rather their built-in incentive is to be as favorable to their corporate clients as possible. It is a...

Gurus' Results Stay Consistently Bad  — Forbes
Investment gurus make their money selling market predictions, not following them. Their overall performance has been historically and consistently dismal. Why people pay for market predictions is a one of Wall Street's biggest mysteries.

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