New book: "Neural Networks and Machine Learning"

[Christopher Bishop]

New book:

        "Neural Networks and Machine Learning"

               Christopher M. Bishop (Ed.)

                       Springer-Verlag

     (for information on how to order: see below)


Contents:

B. D. Ripley:
" Statistical Principles of Model Fitting"

L. Brieman:
"Bias-Variance, Regularization, Instability and Stabilization"

J. M. Buhman and N. Tishby:
"Empirical Risk Optimization: A Statistical Learning Theory of Data
Clustering"

E. D. Sontag:
"VC Dimension of Neural Networks"

R. M. Neal:
"Assessing Relevance Determination Methods using DELVE"

D. J. C. MacKay:
"Introduction to Gaussian Processes"

H. Zhu, C. K. I. Williams, R. Rohwer and M. Morciniec:
"Gaussian Regression and Optimal Finite Dimensional Linear Models"

T. S. Jaakkola and M. I. Jordan:
"Variational Methods and the QMR-DT Database"

D. Barber and C. M. Bishop:
"Ensemble Learning in Bayesian Neural Networks"

V. Vapnik:
"The Support Vector Method of Function Estimation"

B. Sallans, G. E. Hinton and Z. Ghahramani:
"A Hierarchical Community of Experts"

E. B. Baum:
"Manifesto for an Evolutionary Economics of Intelligence"


Preface: