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» A Framework for Machine Learning with Ambiguous Objects
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142
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CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
16 years 10 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
106
Voted
WACV
2008
IEEE
15 years 9 months ago
Likelihood Map Fusion for Visual Object Tracking
Visual object tracking can be considered as a figure-ground classification task. In this paper, different features are used to generate a set of likelihood maps for each pixel i...
Zhaozheng Yin, Fatih Porikli, Robert T. Collins
105
Voted
TAL
2004
Springer
15 years 8 months ago
Smoothing and Word Sense Disambiguation
This paper presents an algorithm to apply the smoothing techniques described in [1] to three different Machine Learning (ML) methods for Word Sense Disambiguation (WSD). The method...
Eneko Agirre, David Martínez
132
Voted
ICPR
2010
IEEE
15 years 22 days ago
Learning the Kernel Combination for Object Categorization
Although Support Vector Machines(SVM) succeed in classifying several image databases using image descriptors proposed in the literature, no single descriptor can be optimal for ge...
Deyuan Zhang, Xiaolong Wang, Bingquan Liu
152
Voted
SEAL
2010
Springer
15 years 20 days ago
Dominance-Based Pareto-Surrogate for Multi-Objective Optimization
Abstract. Mainstream surrogate approaches for multi-objective problems build one approximation for each objective. Mono-surrogate approaches instead aim at characterizing the Paret...
Ilya Loshchilov, Marc Schoenauer, Michèle S...