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» A Framework for Multiple-Instance Learning
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FLAIRS
2006
13 years 10 months ago
Using Active Relocation to Aid Reinforcement Learning
We propose a new framework for aiding a reinforcement learner by allowing it to relocate, or move, to a state it selects so as to decrease the number of steps it needs to take in ...
Lilyana Mihalkova, Raymond J. Mooney
NIPS
2004
13 years 10 months ago
Semi-supervised Learning on Directed Graphs
Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining u...
Dengyong Zhou, Bernhard Schölkopf, Thomas Hof...
ACL
2010
13 years 7 months ago
Learning Common Grammar from Multilingual Corpus
We propose a corpus-based probabilistic framework to extract hidden common syntax across languages from non-parallel multilingual corpora in an unsupervised fashion. For this purp...
Tomoharu Iwata, Daichi Mochihashi, Hiroshi Sawada
ICCV
2003
IEEE
14 years 11 months ago
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood ...
Shuicheng Yan, Mingjing Li, HongJiang Zhang, QianS...
TIP
2010
182views more  TIP 2010»
13 years 4 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...