217 search results - page 23 / 44 » Training Hard-Margin Support Vector Machines Using Greedy St... |
MP
2006
13 years 7 months ago
2006
There are many applications related to singly linearly constrained quadratic programs subjected to upper and lower bounds. In this paper, a new algorithm based on secant approximat...
RECOMB
2005
Springer
14 years 8 months ago
2005
Springer
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
PKDD
2009
Springer
14 years 2 months ago
2009
Springer
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
KDD
2002
ACM
14 years 8 months ago
2002
ACM
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...