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Corpus-Based Techniques for Word Sense Disambiguation
(1998-05-27)
The need for robust and easily extensible systems for word sense disambiguation coupled with successes in training systems for a variety of tasks using large on-line corpora has led to extensive research into corpus-based ...
Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects
(1996-11-01)
We have developed a new Bayesian framework for visual object recognition which is based on the insight that images of objects can be modeled as a conjunction of local features. This framework can be used to both derive ...
Parallel Function Application on a DNA Substrate
(1996-12-01)
In this paper I present a new model that employs a biological (specifically DNA -based) substrate for performing computation. Specifically, I describe strategies for performing parallel function application in the ...
General Purpose Parallel Computation on a DNA Substrate
(1996-12-01)
In this paper I describe and extend a new DNA computing paradigm introduced in Blumberg for building massively parallel machines in the DNA-computing models described by Adelman, Cai et. al., and Liu et. al. Employing ...
Object Detection in Images by Components
(1999-08-11)
In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is ...
Neural Networks
(1996-03-13)
We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes
(1996-01-18)
We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called ...
Factorial Hidden Markov Models
(1996-02-09)
We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum ...
Active Learning with Statistical Models
(1995-03-21)
For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be ...
A Note on the Generalization Performance of Kernel Classifiers with Margin
(2000-05-01)
We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The ...