119 search results - page 12 / 24 » Sparse kernel methods for high-dimensional survival data |
229
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SDM
2011
SIAM
14 years 5 months ago
2011
SIAM
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
133
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TIP
2010
14 years 9 months ago
2010
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
136
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ICML
2004
IEEE
16 years 3 months ago
2004
IEEE
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
140
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JMLR
2010
15 years 1 months ago
2010
Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pa...
118
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ICML
2009
IEEE
16 years 3 months ago
2009
IEEE
Sparsity is a desirable property in high dimensional learning. The 1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining the...