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» On the Complexity of Function Learning
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ICPR
2006
IEEE
16 years 1 months ago
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
It is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. Provided with sufficient training data the discr...
B. Michael Kelm, Chris Pal, Andrew McCallum
JMLR
2002
106views more  JMLR 2002»
15 years 4 days ago
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
ICML
2005
IEEE
16 years 1 months ago
Why skewing works: learning difficult Boolean functions with greedy tree learners
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
CIKM
2009
Springer
15 years 7 months ago
L2 norm regularized feature kernel regression for graph data
Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined fr...
Hongliang Fei, Jun Huan
106
Voted
ISAAC
2010
Springer
240views Algorithms» more  ISAAC 2010»
14 years 10 months ago
Interpretation of Stream Programs: Characterizing Type 2 Polynomial Time Complexity
We study polynomial time complexity of type 2 functionals. For that purpose, we introduce a first order functional stream language. We give criteria, named well-founded, on such pr...
Hugo Férée, Emmanuel Hainry, Mathieu...