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» Optimization on Support Vector Machines
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ML
2002
ACM
146views Machine Learning» more  ML 2002»
15 years 2 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
ICMLA
2010
15 years 1 months ago
An All-at-once Unimodal SVM Approach for Ordinal Classification
Abstract--Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since ...
Joaquim F. Pinto da Costa, Ricardo Sousa, Jaime S....
134
Voted
ICMLA
2009
15 years 1 months ago
Text Classification Methodologies Applied to Micro-Text in Military Chat
We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologi...
Kevin Dela Rosa, Jeffrey Ellen
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
15 years 4 months ago
Managing team-based problem solving with symbiotic bid-based genetic programming
Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team member...
Peter Lichodzijewski, Malcolm I. Heywood
140
Voted
ICML
2009
IEEE
16 years 4 months ago
Online dictionary learning for sparse coding
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...