Stephen José Hanson

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Proceedings of the 4th International Conference on Neural Information Processing Systems

Richard P. Lippmann, John E. Moody, Stephen José Hanson, 1991 .

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

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

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

Stephen José Hanson, Russell A Poldrack, Yaroslav O Halchenko, 2009, Psychological science.

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 Comparision of RBF and MLP Networks for Classification of Biomagnetic Fields

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

Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

Stephen José Hanson, Toshihiko Matsuka, James V. Haxby, 2004, NeuroImage.

Multimodal Integration : fMRI , MRI , EEG , MEG

Barak A. Pearlmutter, Yaroslav O. Halchenko, Stephen José Hanson, 2022 .

Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects

Ronald L. Rivest, Stephen José Hanson, George A. Drastal, 1994, Annual Conference Computational Learning Theory.

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

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

Connectionist modeling and brain function : the developing interface

Stephen José Hanson, Carl R. Olson, 1990 .

Advances in Neural Information Processing Systems 5, [NIPS Conference]

C. Lee Giles, Jack D. Cowan, Stephen José Hanson, 1992 .

Advancing functional connectivity research from association to causation

Daniele Marinazzo, Michael W. Cole, Stephen José Hanson, 2019, Nature Neuroscience.

Computational Learning Theory and Natural Learning

Stephen José Hanson, Jude Shavlik, Systems-Thomas Petsche, 1996 .

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

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

Proceedings of the International Meeting on Advances in Learning Held in Les Arcs, France 28 July-1 August 1986,

Stephen José Hanson, Christel Vrain, Malcolm Bauer, 1986 .

Machine Learning: From Theory to Applications

Ronald L. Rivest, Stephen José Hanson, Werner Remmele, 1993, Lecture Notes in Computer Science.

Efficient Learning of Regular Expressions from Approximate Examples

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

Constraints and prospects

Ronald L. Rivest, Stephen José Hanson, George A. Drastal, 1994 .

Fast Perceptual Learning of Motion in Humans and Neural Networks

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

Using Neural Networks to Modularize Software

Robert W. Schwanke, Stephen José Hanson, S. Hanson, 1994, Machine Learning.

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 .

Commentary on "Reliable Reasoning"

Stephen José Hanson, 2009 .

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 .

Proceedings of the 5th International Conference on Neural Information Processing Systems

C. L. Giles, Jack D. Cowan, Stephen José Hanson, 1992 .

What Makes Derivational Analogy Work: An Experience Report Using APU

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

A stochastic version of the delta rule

Stephen José Hanson, S. Hanson, 1990 .

Machine Learning: From Theory to Applications : Cooperative Research at Siemens and Mit

Ronald L. Rivest, Stephen José Hanson, Werner Remmele, 1993 .

The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise

Stephen José Hanson, Noah Frazier-Logue, 2020, Neural Computation.

Selecting good models

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

9 – CONCEPTUAL CLUSTERING AND CATEGORIZATION: Bridging the Gap between Induction and Causal Models

Stephen José Hanson, 1990 .

Scaling to Domains with Irrelevant Features

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

Backpropagation: some comments and variations

Stephen José Hanson, S. Hanson, 1995 .

What connectionist models learn: Learning and representation in connectionist networks

Stephen José Hanson, David J. Burr, S. Hanson, 1990, Behavioral and Brain Sciences.

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 .

Dense mode clustering in brain maps.

Stephen José Hanson, Yaroslav O Halchenko, Catherine Hanson, 2007, Magnetic resonance imaging.

Advances in Neural Information Processing Systems 30

Michael I. Jordan, Stephen José Hanson, S. Hanson, 1995 .

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 .

Foundational issues in human brain mapping

Stephen José Hanson, Martin Bunzl, S. Hanson, 2010 .

Conceptual Clustering, Categorization, and Polymorphy

Stephen José Hanson, Malcolm Bauer, S. Hanson, 1989, Machine Learning.

Modeling Causal Relationship Between Brain Regions Within the Drug-Cue Processing Network in Chronic Cocaine Smokers

Suchismita Ray, Stephen José Hanson, Bharat Biswal, 2015, Neuropsychopharmacology.

High Resolution Imaging of the Fusiform Face Area (FFA) Using Nonlinear Classifiers Shows Diagnosticity for Nonface Categories

Stephen José Hanson, Arielle Schmidt, 2008 .

Regulation during challenge: A general model of learned performance under schedule constraint.

Stephen José Hanson, William Timberlake, S. Hanson, 1983 .

A Review of: “Neural Networks and Natural Intelligence: Notes from Mudville” Stephen Grossberg (Ed.) Cambridge, MA: MIT/Bradford, 1989 ISBN 0-262-07107-x, 544pp., £31.95

Stephen José Hanson, Carl R. Olson, 1991 .

Central place foraging in Rattus norvegicus

Stephen José Hanson, Peter R. Killeen, S. Hanson, 1981, Animal Behaviour.

A peer-to-peer database for brain imaging data

Stephen José Hanson, Benjamin Martin Bly, Donovan Rebbechi, 2001, NeuroImage.

Attentional Bias in Human Category Learning: The Case of Deep Learning

Stephen José Hanson, Leyla Roskan Caglar, Catherine Hanson, 2018, Front. Psychol..

Categorization in Neuroscience: Brain Response to Objects and Events

Stephen José Hanson, Catherine Hanson, S. Hanson, 2005 .