1345 search results - page 125 / 269 » Efficient Learning of Relational Object Class Models |
123
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ICML
2007
IEEE
16 years 4 months ago
2007
IEEE
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Ne...
114
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UAI
2001
15 years 4 months ago
2001
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
148
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JMLR
2002
15 years 2 months ago
2002
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...
146
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KDD
2009
ACM
16 years 3 months ago
2009
ACM
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
123
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AAAI
2008
15 years 5 months ago
2008
Direction relations between extended spatial objects are important commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, called Cardinal Direction Calculus...