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ML
2002
ACM
140views Machine Learning» more  ML 2002»
13 years 7 months ago
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...
ICASSP
2009
IEEE
13 years 5 months ago
Phonological features in discriminative classification of dysarthric speech
In an attempt to overcome problems associated with articulatory limitations and generative models, this work considers the use of phonological features in discriminative models fo...
Frank Rudzicz
ICMCS
2009
IEEE
146views Multimedia» more  ICMCS 2009»
13 years 5 months ago
Deep networks for audio event classification in soccer videos
In this work is presented a novel approach for the classification of audio concepts in broadcast soccer videos using deep belief network (DBN), a probabilistic neural network with...
Lamberto Ballan, Alessio Bazzica, Marco Bertini, A...
JMLR
2010
159views more  JMLR 2010»
13 years 2 months ago
Semi-Supervised Learning with Max-Margin Graph Cuts
This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic fun...
Branislav Kveton, Michal Valko, Ali Rahimi, Ling H...
UAI
2000
13 years 9 months ago
Variational Relevance Vector Machines
The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions...
Christopher M. Bishop, Michael E. Tipping