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Support Vector Machines: Training and Applications
(1997-03-01)
The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique ...
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 ...
A Note on Support Vector Machines Degeneracy
(1999-08-11)
When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM ...
An Equivalence Between Sparse Approximation and Support Vector Machines
(1997-05-01)
In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, ...
A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks
(1994-04-01)
We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning ...
The Kineticist's Workbench: Combining Symbolic and Numerical Methods in the Simulation of Chemical Reaction Mechanisms
(1991-05-01)
The Kineticist's Workbench is a program that simulates chemical reaction mechanisms by predicting, generating, and interpreting numerical data. Prior to simulation, it analyzes a given mechanism to predict that ...
Convergence Rates of Approximation by Translates
(1992-03-01)
In this paper we consider the problem of approximating a function belonging to some funtion space Φ by a linear comination of n translates of a given function G. Ussing a lemma by Jones (1990) and Barron (1991) we show ...
Pose Determination of a Grasped Object Using Limited Sensing
(1991-05-01)
This report explores methods for determining the pose of a grasped object using only limited sensor information. The problem of pose determination is to find the position of an object relative to the hand. The information ...
Dense Depth Maps from Epipolar Images
(1996-11-01)
Recovering three-dimensional information from two-dimensional images is the fundamental goal of stereo techniques. The problem of recovering depth (three-dimensional information) from a set of images is essentially the ...
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 ...