Sciweavers

164 search results - page 9 / 33
» Cost-conscious multiple kernel learning
Sort
View
120
Voted
NIPS
2004
15 years 1 months ago
Computing regularization paths for learning multiple kernels
The problem of learning a sparse conic combination of kernel functions or kernel matrices for classification or regression can be achieved via the regularization by a block 1-norm...
Francis R. Bach, Romain Thibaux, Michael I. Jordan
97
Voted
DAGM
2010
Springer
15 years 23 days ago
Random Fourier Approximations for Skewed Multiplicative Histogram Kernels
Abstract. Approximations based on random Fourier features have recently emerged as an efficient and elegant methodology for designing large-scale kernel machines [4]. By expressing...
Fuxin Li, Catalin Ionescu, Cristian Sminchisescu
90
Voted
ICML
2009
IEEE
16 years 14 days ago
More generality in efficient multiple kernel learning
Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient desce...
Manik Varma, Bodla Rakesh Babu
JMLR
2011
157views more  JMLR 2011»
14 years 6 months ago
Variable Sparsity Kernel Learning
This paper1 presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assu...
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattac...
ICML
2007
IEEE
16 years 14 days ago
Multiclass multiple kernel learning
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kern...
Alexander Zien, Cheng Soon Ong