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» Learning Subjective Functions with Large Margins
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MIR
2005
ACM
133views Multimedia» more  MIR 2005»
15 years 3 months ago
Probabilistic web image gathering
We propose a new method for automated large scale gathering of Web images relevant to specified concepts. Our main goal is to build a knowledge base associated with as many conce...
Keiji Yanai, Kobus Barnard
ATAL
2010
Springer
14 years 11 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
14 years 10 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
SIGIR
2006
ACM
15 years 3 months ago
Adapting ranking SVM to document retrieval
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must ...
Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Hua...
GECCO
2010
Springer
152views Optimization» more  GECCO 2010»
15 years 2 months ago
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the “toolbox” used by ...
Jean-Baptiste Mouret, Stéphane Doncieux, Be...