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» Experimental perspectives on learning from imbalanced data
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NIPS
2007
14 years 11 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
PR
2010
156views more  PR 2010»
14 years 8 months ago
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel met...
Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zh...
ICRA
1998
IEEE
106views Robotics» more  ICRA 1998»
15 years 2 months ago
Learning a Linear Association of Drilling Profiles in Stapedotomy Surgery
The two-level fuzzy-lattice (2L-FL) learning scheme is introduced for application on an intelligent surgical (mechatronic) drill in the stapedotomy surgical procedure in the ear. ...
Vassilis G. Kaburlasos, Vassilios Petridis, Peter ...
CSL
2010
Springer
14 years 10 months ago
Discriminative training of HMMs for automatic speech recognition: A survey
Recently, discriminative training (DT) methods have achieved tremendous progress in automatic speech recognition (ASR). In this survey article, all mainstream DT methods in speech...
Hui Jiang
ICAC
2006
IEEE
15 years 3 months ago
Learning Application Models for Utility Resource Planning
Abstract— Shared computing utilities allocate compute, network, and storage resources to competing applications on demand. An awareness of the demands and behaviors of the hosted...
Piyush Shivam, Shivnath Babu, Jeffrey S. Chase