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TNN
2008
182views more  TNN 2008»
13 years 7 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
SSPR
2010
Springer
13 years 6 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
PKDD
2010
Springer
138views Data Mining» more  PKDD 2010»
13 years 6 months ago
Constructing Nonlinear Discriminants from Multiple Data Views
There are many situations in which we have more than one view of a single data source, or in which we have multiple sources of data that are aligned. We would like to be able to bu...
Tom Diethe, David R. Hardoon, John Shawe-Taylor
ICPR
2008
IEEE
14 years 2 months ago
IR and visible face recognition using fusion of kernel based features
In this paper we present the face recognition method using feature-level fusion where the infrared (IR) and visible face images are fused at transformed domain. The main contribut...
Shahbe Mat Desa, Subhas Hati
NIPS
2008
13 years 9 months ago
Kernel Change-point Analysis
We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an unlabelled sample of observations consists in,...
Zaïd Harchaoui, Francis Bach, Eric Moulines