Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
A Simple Decomposition Method for Support Vector Machines
Machine Learning
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
The analysis of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
Text mining techniques for leveraging positively labeled data
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Distributed tuning of machine learning algorithms using MapReduce Clusters
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
HyParSVM: a new hybrid parallel software for support vector machine learning on SMP clusters
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Data mining with parallel support vector machines for classification
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Automatic shape optimisation of pharmaceutical tablets using Partial Differential Equations
Computers and Structures
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Support vector machines are powerful kernel methods for classification and regression tasks. If trained optimally, they produce excellent separating hyperplanes. The quality of the training, however, depends not only on the given training data but also on additional learning parameters, which are difficult to adjust, in particular for unbalanced datasets. Traditionally, grid search techniques have been used for determining suitable values for these parameters. In this paper, we propose an automated approach to adjusting the learning parameters using a derivative-free numerical optimizer. To make the optimization process more efficient, a new sensitive quality measure is introduced. Numerical tests with a well-known dataset show that our approach can produce support vector machines that are very well tuned to their classification tasks.