93 search results - page 8 / 19 » Maximal Discrepancy for Support Vector Machines |
KDD
2004
ACM
14 years 7 months ago
2004
ACM
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
SWARM
2010
SPRINGER
13 years 5 months ago
2010
SPRINGER
This paper proposes an immune concentration based virus detection approach which utilizes a two-element concentration vector to construct the feature. In this approach, ‘self’ ...
PKDD
2009
Springer
14 years 1 months ago
2009
Springer
The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fi...
CMIG
2010
13 years 2 months ago
2010
This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vector Machines (SVMs). Each spot is a small region on the microarray surface where cha...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributio...