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ICIAP
2005
ACM
16 years 6 months ago
A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a ne...
Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti...
ICANN
2007
Springer
16 years 19 days ago
Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...
Shigeo Abe, Kenta Onishi
SSPR
2004
Springer
15 years 11 months ago
An MCMC Feature Selection Technique for Characterizing and Classifying Spatial Region Data
We focus on characterizing spatial region data when distinct classes of structural patterns are present. We propose a novel statistical approach based on a supervised framework for...
Despina Kontos, Vasileios Megalooikonomou, Marc J....
NIPS
1992
15 years 7 months ago
Some Solutions to the Missing Feature Problem in Vision
In visual processing the ability to deal with missing and noisy information is crucial. Occlusions and unreliable feature detectors often lead to situations where little or no dir...
Subutai Ahmad, Volker Tresp
ICMLA
2009
15 years 4 months ago
ECON: A Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approx
This paper introduces a new algorithm, namely the EquiCorrelation Network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, b...
Manuel Loth, Philippe Preux, Samuel Delepoulle, Ch...