Sciweavers

NIPS
2000
13 years 5 months ago
A New Approximate Maximal Margin Classification Algorithm
A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alm...
Claudio Gentile
NIPS
2000
13 years 5 months ago
Learning Joint Statistical Models for Audio-Visual Fusion and Segregation
People can understand complex auditory and visual information, often using one to disambiguate the other. Automated analysis, even at a lowlevel, faces severe challenges, includin...
John W. Fisher III, Trevor Darrell, William T. Fre...
NIPS
2000
13 years 5 months ago
Discovering Hidden Variables: A Structure-Based Approach
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As s...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koll...
NIPS
2000
13 years 5 months ago
A Productive, Systematic Framework for the Representation of Visual Structure
We describe a unified framework for the understanding of structure representation in primate vision. A model derived from this framework is shown to be effectively systematic in t...
Shimon Edelman, Nathan Intrator
NIPS
2000
13 years 5 months ago
High-temperature Expansions for Learning Models of Nonnegative Data
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Oliver B. Downs
NIPS
2000
13 years 5 months ago
Feature Correspondence: A Markov Chain Monte Carlo Approach
When trying to recover 3D structure from a set of images, the most di cult problem is establishing the correspondence between the measurements. Most existing approaches assume tha...
Frank Dellaert, Steven M. Seitz, Sebastian Thrun, ...
NIPS
2000
13 years 5 months ago
Explaining Away in Weight Space
Explaining away has mostly been considered in terms of inference of states in belief networks. We show how it can also arise in a Bayesian context in inference about the weights g...
Peter Dayan, Sham Kakade