References

for the Montreal Workshop and Spring School on Neural Nets and Learning Algorithms






Neural networks and approximation theory

F. Girosi

References in Federico Girosi's home page: here, which include an electronic version of his slides.


Learning to be a dynamical system

G. Dreyfus

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General methods for training ensembles of regressors

N. Intrator

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Constructive learning algorithms: empirical study of learning curves

J.-P. Nadal

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Introduction to Hidden Markov Models

S. Bengio

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Input/Output Hidden Markov Models

S. Bengio

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Introduction to graphical models and neural networks

M. Jordan

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Unsupervised learning and vision

S. Becker

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An Introduction to Suspiciousness - Part I: Optimal convergence of learning algorithms

M. Gori

All papers can be recovered at DSI Labs

An Introduction to Suspiciousness - Part II: A general approach to learning and problem solving

M. Gori

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Number-Plate Recognition with Neural Networks

M. Gori

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Flexible Methods for Classification

T. Hastie

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Statistical methods for learning nonoverlapping neural networks

M. Marchand

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Part I: Regression shrinkage and selection via the lasso - Part II: Model search and inference by bootstrap "bumping"

R. Tibshirani

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Factor Analysis and the Helmholtz Machine

P. Dayan

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Bias and Variance in TD Learning

P. Dayan

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