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» Learning from Highly Structured Data by Decomposition
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NIPS
2001
15 years 5 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi
129
Voted
ICML
2006
IEEE
16 years 4 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
MLDM
2007
Springer
15 years 10 months ago
Nonlinear Feature Selection by Relevance Feature Vector Machine
Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between feat...
Haibin Cheng, Haifeng Chen, Guofei Jiang, Kenji Yo...
129
Voted
LREC
2010
149views Education» more  LREC 2010»
15 years 5 months ago
Paragraph Acquisition and Selection for List Question Using Amazon's Mechanical Turk
Creating more fine-grained annotated data than previously relevent document sets is important for evaluating individual components in automatic question answering systems. In this...
Fang Xu, Dietrich Klakow
111
Voted
ICMLA
2008
15 years 5 months ago
Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes
When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into intere...
Cecilia Sönströd, Ulf Johansson, Ulf Nor...