Thomas Petsche

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Learning Priorities From Noisy Examples

Geoffrey G. Towell, Thomas Petsche, Michael R. Miller, 2000, ICML.

Recurrent Neural Networks with Continuous Topology Adaptation, Kalman Filter Based Training

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Incrementally Learning Time-Varying Half Planes

Ronald L. Rivest, Anthony Kuh, Thomas Petsche, 1991, NIPS.

Abnormal Data Points in the Data Set: An Algorithm For Robust Neural Net Regression

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Initializing Neural Networks Using Decision Trees

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

A Practical Approach for Evaluating Generalization Performance

Russell Greiner, Thomas Petsche, Stephen José Hanson, 1997 .

A Comparision of RBF and MLP Networks for Classification of Biomagnetic Fields

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Computational learning theory and natural learning systems: Volume IV: making learning systems practical

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997, COLT 1997.

A Trellis-Structured Neural Network

Bradley W. Dickinson, Thomas Petsche, 1987, NIPS.

System Theoretic Models for High Density VLSI Structures

Kenneth Steiglitz, Anthony Kuh, Bradley W. Dickinson, 1989 .

A Neural Network Autoassociator for Induction Motor Failure Prediction

Stephen Jose Hanson, Thomas Petsche, Christian J. Darken, 1995, NIPS.

Communication Flow Expressions in a Notification and Response System

Joann J. Ordille, Thomas Petsche, 2002, TES.

Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996

Michael I. Jordan, Michael C. Mozer, Thomas Petsche, 1997, NIPS.

Advances in neural information processing systems : proceedings of the ... conference

Thomas G. Dietterich, Richard P. Lippmann, Klaus-Robert Müller, 1989 .

Learning in time varying environments

Anthony Kuh, Thomas Petsche, T. Petsche, 2000 .

Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

Bradley W. Dickinson, Thomas Petsche, 1990, IEEE Trans. Neural Networks.

Trellis Codes, Receptive Fields, and Fault Tolerant, Self-Repairing Neural Networks

Bradley W. Dickinson, Thomas Petsche, 1993, Machine Learning: From Theory to Applications.

Efficient Learning of Regular Expressions from Approximate Examples

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Fast Perceptual Learning of Motion in Humans and Neural Networks

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Intersections between theory and experiment

Ronald L. Rivest, Stephen José Hanson, Thomas Petsche, 1994 .

Computational learning theory and natural learning systems

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Supervised Learning Using Labeled and Unlabeled Examples

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

On Learning the Neural Network Architecture: A Case Study

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

AN AUTOASSOCIATOR FOR ON-LINE MOTOR MONITORING

Stephen José Hanson, Thomas Petsche, Gary M. Kuhn, 1998 .

What Makes Derivational Analogy Work: An Experience Report Using APU

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Selecting good models

Stephen José Hanson, Jude Shavlik, Thomas Petsche, 1995 .

Scaling to Domains with Irrelevant Features

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment

Ronald L. Rivest, Stephen José Hanson, Thomas Petsche, 1994 .

Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Dynamic Modeling of Chaotic Time Series by Neural Networks

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

Learning Time-Varying Concepts

Ronald L. Rivest, Anthony Kuh, Thomas Petsche, 1990, NIPS.

Probabilistic Self-structuring and Learning

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .

N-Learners Problem: System of PAC Learners

Russell Greiner, Stephen José Hanson, Thomas Petsche, 1997 .