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118
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NIPS
2003
15 years 2 months ago
Sequential Bayesian Kernel Regression
We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the num...
Jaco Vermaak, Simon J. Godsill, Arnaud Doucet
95
Voted
NIPS
2003
15 years 2 months ago
Salient Boundary Detection using Ratio Contour
This paper presents a novel graph-theoretic approach, named ratio contour, to extract perceptually salient boundaries from a set of noisy boundary fragments detected in real image...
Song Wang, Toshiro Kubota, Jeffrey Mark Siskind
NIPS
2003
15 years 2 months ago
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks
Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are frequently used to overcome these difficulties. We ...
Justin Werfel, Xiaohui Xie, H. Sebastian Seung
111
Voted
NIPS
2003
15 years 2 months ago
Perspectives on Sparse Bayesian Learning
Recently, relevance vector machines (RVM) have been fashioned from a sparse Bayesian learning (SBL) framework to perform supervised learning using a weight prior that encourages s...
David P. Wipf, Jason A. Palmer, Bhaskar D. Rao
151
Voted
NIPS
2003
15 years 2 months ago
Learning a Rare Event Detection Cascade by Direct Feature Selection
Face detection is a canonical example of a rare event detection problem, in which target patterns occur with much lower frequency than nontargets. Out of millions of face-sized wi...
Jianxin Wu, James M. Rehg, Matthew D. Mullin