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144
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ICML
2004
IEEE
15 years 9 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
149
Voted
ML
2006
ACM
121views Machine Learning» more  ML 2006»
15 years 3 months ago
Model-based transductive learning of the kernel matrix
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
165
Voted
ICRA
2010
IEEE
104views Robotics» more  ICRA 2010»
15 years 2 months ago
Using model knowledge for learning inverse dynamics
— In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in ...
Duy Nguyen-Tuong, Jan Peters
135
Voted
HCI
2007
15 years 5 months ago
Learning Topologies of Situated Public Displays by Observing Implicit User Interactions
In this paper we present a procedure to learn a topological model of Situated Public Displays from data of people traveling between these displays. This model encompasses the dista...
Hans Jörg Müller, Antonio Krüger
165
Voted
NIPS
2008
15 years 4 months ago
A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
We present an approach that combines bag-of-words and spatial models to perform semantic and syntactic analysis for recognition of an object based on its internal appearance and i...
Abhinav Gupta, Jianbo Shi, Larry S. Davis