ICASSP
2011
IEEE
13 years 2 months ago
2011
IEEE
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
TSP
2008
13 years 10 months ago
2008
We consider the problem of estimating the covariance matrix of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly ...
TNN
2008
13 years 10 months ago
2008
This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where tr...
TNN
2008
13 years 10 months ago
2008
Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
CVPR
2008
IEEE
13 years 11 months ago
2008
IEEE
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...
ICONIP
2008
14 years 9 days ago
2008
A number of learning machines used in information science are not regular, but rather singular, because they are non-identifiable and their Fisher information matrices are singula...
ICMLA
2008
14 years 9 days ago
2008
In this paper, we introduce two new formulations for multi-class multi-kernel relevance vector machines (mRVMs) that explicitly lead to sparse solutions, both in samples and in nu...
AAAI
2007
14 years 1 months ago
2007
We introduce a new collaborative machine learning paradigm in which the user directs a learning algorithm by manually editing the automatically induced model. We identify a generi...
DRR
2010
14 years 1 months ago
2010
Scaling up document-image classifiers to handle an unlimited variety of document and image types poses serious challenges to conventional trainable classifier technologies. Highly...
CSB
2004
IEEE
14 years 2 months ago
2004
IEEE
Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computatio...