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» Learning Spectral Clustering, With Application To Speech Sep...
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91
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ICASSP
2009
IEEE
15 years 6 months ago
Small-group learning projects to make signal processing more appealing: From speech processing to OFDMA synchronization
Whereas lecturing is the most widely used mode of instruction, we have explored small-group learning projects to make signal processing more appealing at the University and in Eng...
G. Ferre, Audrey Giremus, Eric Grivel
122
Voted
PAMI
2007
202views more  PAMI 2007»
14 years 11 months ago
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
—A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods....
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
83
Voted
NIPS
1994
15 years 28 days ago
A Non-linear Information Maximisation Algorithm that Performs Blind Separation
A new learning algorithmis derived which performs online stochastic gradient ascent in the mutual informationbetween outputs and inputs of a network. In the absence of a priori kn...
Anthony J. Bell, Terrence J. Sejnowski
PR
2006
164views more  PR 2006»
14 years 11 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung
91
Voted
FSMNLP
2008
Springer
15 years 1 months ago
Learning with Weighted Transducers
Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine ...
Corinna Cortes, Mehryar Mohri