99 search results - page 13 / 20 » Learning the kernel via convex optimization |
ICML
2006
IEEE
16 years 14 days ago
2006
IEEE
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...
110
Voted
PKDD
2010
Springer
14 years 10 months ago
2010
Springer
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplaci...
105
Voted
ICML
2009
IEEE
16 years 14 days ago
2009
IEEE
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
NIPS
2007
15 years 1 months ago
2007
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
NIPS
2008
15 years 1 months ago
2008
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...