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ICCV
2003
IEEE
14 years 10 months ago
Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weighted-based Approach
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science. Examples in computer vision...
Lior Wolf, Amnon Shashua
BIOINFORMATICS
2007
151views more  BIOINFORMATICS 2007»
13 years 8 months ago
A new protein-protein docking scoring function based on interface residue properties
Motivation: Protein–protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low s...
Julie Bernauer, Jérôme Azé, Jo...
EPIA
2009
Springer
13 years 11 months ago
Semantic Image Search and Subset Selection for Classifier Training in Object Recognition
Abstract. Robots need to ground their external vocabulary and internal symbols in observations of the world. In recent works, this problem has been approached through combinations ...
Rui Pereira, Luís Seabra Lopes, Augusto Sil...
IJON
2007
131views more  IJON 2007»
13 years 7 months ago
Margin-based active learning for LVQ networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples and thereby increase speed a...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
PRL
1998
132views more  PRL 1998»
13 years 7 months ago
Unsupervised feature selection using a neuro-fuzzy approach
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a ¯exible membersh...
Jayanta Basak, Rajat K. De, Sankar K. Pal