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IJCAI
2003
14 years 11 months ago
Evaluating Matching Algorithms: the Monotonicity Principle
In this paper we present the monotonicity principle, a sufficient condition to ensure that exact mapping, a mapping as would be performed by a human observer, is ranked close to ...
Ateret Anaby-Tavor, Avigdor Gal, Alberto Trombetta
ICML
2004
IEEE
15 years 3 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
NIPS
2003
14 years 11 months ago
Learning to Find Pre-Images
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solu...
Gökhan H. Bakir, Jason Weston, Bernhard Sch&o...
ICRA
1998
IEEE
143views Robotics» more  ICRA 1998»
15 years 2 months ago
Maximum Likelihood Rover Localization by Matching Range Maps
This paper describes maximum likelihood estimation techniques for performing rover localization in natural terrain by matching range maps. An occupancy map of the local terrain is...
Clark F. Olson, Larry Matthies
ICPR
2002
IEEE
15 years 11 months ago
To Each According to its Need: Kernel Class Specific Classifiers
We present in this paper a new multi-class Bayes classifier that permits using separate feature vectors, chosen specifically for each class. This technique extends previous work o...
Barbara Caputo, Heinrich Niemann