Advances in Neural Information Processing Systems 4

Editions published in Advances in Neural Information Processing Systems 4 144

Optical Implementation of a Self-Organizing Feature Extractor
Principles of Risk Minimization for Learning Theory
Bayesian Model Comparison and Backprop Nets
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems
Constant-Time Loading of Shallow 1-Dimensional Networks
Experimental Evaluation of Learning in a Neural Microsystem
Gradient Descent: Second Order Momentum and Saturating Error
Threshold Network Learning in the Presence of Equivalences
Tangent Prop - A formalism for specifying selected invariances in an adaptive network
Polynomial Uniform Convergence of Relative Frequencies to Probabilities
Unsupervised learning of distributions on binary vectors using two layer networks
Incrementally Learning Time-varying Half-planes
The VC-Dimension versus the Statistical Capacity of Multilayer Networks
Some Approximation Properties of Projection Pursuit Learning Networks
A Simple Weight Decay Can Improve Generalization
Neural Computing with Small Weights
Best-First Model Merging for Dynamic Learning and Recognition
Rule Induction through Integrated Symbolic and Subsymbolic Processing
Hierarchies of adaptive experts
Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules
Adaptive Soft Weight Tying using Gaussian Mixtures
Repeat Until Bored: A Pattern Selection Strategy
Towards Faster Stochastic Gradient Search
Competitive Anti-Hebbian Learning of Invariants
Kernel Regression and Backpropagation Training With Noise
Merging Constrained Optimisation with Deterministic Annealing to \"Solve\" Combinatorially Hard Problems
Splines, Rational Functions and Neural Networks
Networks with Learned Unit Response Functions
Learning in Feedforward Networks with Nonsmooth Functions
Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks
Iterative Construction of Sparse Polynomial Approximations

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Advances in Neural Information Processing Systems 4

Editions published in Advances in Neural Information Processing Systems 4 144

Optical Implementation of a Self-Organizing Feature Extractor

Principles of Risk Minimization for Learning Theory

Bayesian Model Comparison and Backprop Nets

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems

Constant-Time Loading of Shallow 1-Dimensional Networks

Experimental Evaluation of Learning in a Neural Microsystem

Gradient Descent: Second Order Momentum and Saturating Error

Threshold Network Learning in the Presence of Equivalences

Tangent Prop - A formalism for specifying selected invariances in an adaptive network

Polynomial Uniform Convergence of Relative Frequencies to Probabilities

Unsupervised learning of distributions on binary vectors using two layer networks

Incrementally Learning Time-varying Half-planes

The VC-Dimension versus the Statistical Capacity of Multilayer Networks

Some Approximation Properties of Projection Pursuit Learning Networks

A Simple Weight Decay Can Improve Generalization

Neural Computing with Small Weights

Best-First Model Merging for Dynamic Learning and Recognition

Rule Induction through Integrated Symbolic and Subsymbolic Processing

Hierarchies of adaptive experts

Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules

Adaptive Soft Weight Tying using Gaussian Mixtures

Repeat Until Bored: A Pattern Selection Strategy

Towards Faster Stochastic Gradient Search

Competitive Anti-Hebbian Learning of Invariants

Kernel Regression and Backpropagation Training With Noise

Merging Constrained Optimisation with Deterministic Annealing to \"Solve\" Combinatorially Hard Problems

Splines, Rational Functions and Neural Networks

Networks with Learned Unit Response Functions

Learning in Feedforward Networks with Nonsmooth Functions

Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks

Iterative Construction of Sparse Polynomial Approximations

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