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» Salient Feature Selection for Visual Concept Learning
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IDA
2007
Springer
14 years 1 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in b...
Pance Panov, Saso Dzeroski
KDD
2005
ACM
168views Data Mining» more  KDD 2005»
14 years 7 months ago
Nomograms for visualizing support vector machines
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan B...
ICMCS
2006
IEEE
181views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Toward Intelligent Use of Semantic Information on Subspace Discovery for Image Retrieval
Image retrieval has been widely used in many fields of science and engineering. The semantic concept of user interest is obtained by a learning process. Traditional techniques oft...
Jie Yu, Qi Tian
MM
2006
ACM
203views Multimedia» more  MM 2006»
14 years 1 months ago
Learning image manifolds by semantic subspace projection
In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...
Jie Yu, Qi Tian
AAAI
2004
13 years 8 months ago
Self-Organizing Visual Maps
This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they ...
Robert Sim, Gregory Dudek