The nature of statistical learning theory
The nature of statistical learning theory
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Guest Editors' Introduction: Data Mining in Bioinformatics
IEEE Intelligent Systems
Margin calibration in SVM class-imbalanced learning
Neurocomputing
SVM classification to distinguish Parkinson disease patients
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Query-adaptive ranking with support vector machines for protein homology prediction
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
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This paper describes our solution for the protein homology prediction task in KDD Cup 2004 competition. This task is modeled as a supervised learning problem with multiple performance metrics. Several key characteristics make the problem both novel and challenging, including the concept of data blocks and the presence of large-scale and imbalanced training data. These features make a naive application of the traditional classification algorithms infeasible. Our approach focuses on making full use of the abundant information within the blocks, and developing a new technique for reducing and balancing training data to make the support vector machine applicable to this kind of large-scale and imbalanced learning tasks.