KDD
2001
ACM
14 years 5 months ago
2001
ACM
Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal s...
KDD
2004
ACM
14 years 5 months ago
2004
ACM
Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks...
KDD
2005
ACM
14 years 5 months ago
2005
ACM
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
KDD
2006
ACM
14 years 5 months ago
2006
ACM
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
ISBI
2004
IEEE
14 years 5 months ago
2004
IEEE
Size Functions and Support Vector Machines are used to implement a new automatic classifier of melanocytic lesions. This is mainly based on a qualitative assessment of asymmetry. ...
ICML
2000
IEEE
14 years 5 months ago
2000
IEEE
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
ICML
2000
IEEE
14 years 5 months ago
2000
IEEE
We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel ma...
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The red...