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» A regularization framework for multiple-instance learning
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CVPR
2011
IEEE
13 years 3 months ago
Bayesian Deblurring with Integrated Noise Estimation
Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequence of MAP estimation, separate pre-process...
Uwe Schmidt, Kevin Schelten, Stefan Roth
PAMI
2010
225views more  PAMI 2010»
13 years 2 months ago
Semi-Supervised Classification via Local Spline Regression
Abstract--This paper presents local spline regression for semisupervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the...
Shiming Xiang, Feiping Nie, Changshui Zhang
INFORMATICALT
2007
111views more  INFORMATICALT 2007»
13 years 7 months ago
Oblique Support Vector Machines
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principl...
Chih-Chia Yao, Pao-Ta Yu
PAMI
2008
391views more  PAMI 2008»
13 years 7 months ago
Riemannian Manifold Learning
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold...
Tong Lin, Hongbin Zha
JMLR
2010
154views more  JMLR 2010»
13 years 2 months ago
Infinite Predictor Subspace Models for Multitask Learning
Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a late...
Piyush Rai, Hal Daumé III