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» Toward Efficient Agnostic Learning
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ICML
2008
IEEE
15 years 10 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
77
Voted
DAWAK
2010
Springer
14 years 9 months ago
Modelling Complex Data by Learning Which Variable to Construct
Abstract. This paper addresses a task of variable selection which consists in choosing a subset of variables that is sufficient to predict the target label well. Here instead of tr...
Françoise Fessant, Aurélie Le Cam, M...
GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
15 years 24 days ago
Efficiently evolving programs through the search for novelty
A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
Joel Lehman, Kenneth O. Stanley
77
Voted
WWW
2004
ACM
15 years 10 months ago
Enhancing the SCORM metadata model
Nowadays, the leading e-learning platforms are converging towards standardization. This paper presents an extension to the SCORM, today's most well acclaimed e-learning stand...
David Simões, Nuno Horta, Rui Luís
UIC
2007
Springer
15 years 3 months ago
Ontology-Based Semantic Recommendation for Context-Aware E-Learning
Nowadays, e-learning systems are widely used for education and training in universities and companies because of their electronic course content access and virtual classroom partic...
Zhiwen Yu, Yuichi Nakamura, Seiie Jang, Shoji Kaji...