66 search results - page 1 / 14 » Mixture Density Mercer Kernels: A Method to Learn Kernels Di... |
SDM
2004
SIAM
13 years 6 months ago
2004
SIAM
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
ICML
2006
IEEE
14 years 5 months 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...
ICML
2004
IEEE
14 years 5 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...
IJCAI
2007
13 years 6 months ago
2007
A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data...
IVC
2010
13 years 3 months ago
2010
In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...