Research Abstracts - Machine Learning

MIT Artificial Intelligence Laboratory
Research Abstracts — 2001

Machine Learning

Jake Beal	Bootstrapping Communications from Shared Experience
Jake Beal, Nick Caldwell, Jimmy Lin, Justin Schmidt, Marc Spraragen & Patrick Winston	The Bridge Project
Nicholas Tung Chan & Adlar Jeewook Kim	Study of Artificial Financial Markets with Adaptive Trading Agents
Adrian Corduneanu & Tommi Jaakkola	Stable Mixing of Complete and Incomplete Information
Leslie Pack Kaelbling, Sarah Finney, Natalia Gardiol & Tim Oates	Learning with Deictic Representations
Adlar Jeewook Kim	Input/Output HiddenMarkovModels for Modeling Stock Order Flows
Attila Kondacs	Constraint Learning
Vinay Kumar	Learning-Based Approach to Estimation of Morphable Model Parameters
Terran Lane & Leslie Pack Kaelbling	Scaling Techniques for Large Markov Decision Process Planning Problems
Sayan Mukherjee	Statistical Test for Similarity Metrics and Clustering
Sayan Mukherjee & Ryan Rifkin	Support Vector Machine Classification of Microarray Data
Luis Perez-Breva	Combining Kernel Machines Through Decorrelation
Luis Perez-Breva, Giorgos Zacharia & Osamu Yoshimi	Extracting Information fromCNN Financial News
Alexander Rakhlin	Combining Classifiers
Jason D. M. Rennie & Ryan Rifkin	Improving Multiclass Text Classification with the Support Vector Machine
Whitman Richards	Shared Mental Models &Decision-Making
Christian R. Shelton	Reinforcement Learning for Electronic Market-Making
Nathan Srebro, David Karger & Tommi Jaakkola	MaximumLikelihood Markov Hypertrees
Martin Szummer, Tommi Jaakkola & Tomaso Poggio	Learning from Partially Labeled Data
Justin Werfel	Biophysically Realistic Models for Learning in Neural Networks
Gene Yeo	Multiclass Classification of SRBCT Tumors

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