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» Sampling Methods for Unsupervised Learning
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147
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ML
2010
ACM
181views Machine Learning» more  ML 2010»
15 years 24 days ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
CVPR
2008
IEEE
16 years 4 months ago
Constrained spectral clustering through affinity propagation
Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate them into traditional clustering methods, such as ...
Miguel Á. Carreira-Perpiñán, ...
ICML
2004
IEEE
15 years 7 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
ECCV
2002
Springer
16 years 4 months ago
Composite Texture Descriptions
Textures can often more easily be described as a composition of subtextures than as a single texture. The paper proposes a way to model and synthesize such "composite textures...
Alexey Zalesny, Vittorio Ferrari, Geert Caenen, Do...
ICML
2010
IEEE
15 years 3 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...