Machine Learning
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
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Profile-Based String Kernels for Remote Homology Detection and Motif Extraction
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A statistical framework for genomic data fusion
Bioinformatics
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Artificial Intelligence in Medicine
The Feature Importance Ranking Measure
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Early prediction of temporal sequences based on information transfer
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Learning bounds for support vector machines with learned kernels
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Multi kernel learning with online-batch optimization
The Journal of Machine Learning Research
Life-logging of wheelchair driving on web maps for visualizing potential accidents and incidents
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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We propose novel algorithms for solving the so-called Support Vector Multiple Kernel Learning problem and show how they can be used to understand the resulting support vector decision function. While classical kernel-based algorithms (such as SVMs) are based on a single kernel, in Multiple Kernel Learning a quadratically-constraint quadratic program is solved in order to find a sparse convex combination of a set of support vector kernels. We show how this problem can be cast into a semi-infinite linear optimization problem which can in turn be solved efficiently using a boosting-like iterative method in combination with standard SVM optimization algorithms. The proposed method is able to deal with thousands of examples while combining hundreds of kernels within reasonable time. In the second part we show how this technique can be used to understand the obtained decision function in order to extract biologically relevant knowledge about the sequence analysis problem at hand. We consider the problem of splice site identification and combine string kernels at different sequence positions and with various substring (oligomer) lengths. The proposed algorithm computes a sparse weighting over the length and the substring, highlighting which substrings are important for discrimination. Finally, we propose a bootstrap scheme in order to reliably identify a few statistically significant positions, which can then be used for further analysis such as consensus finding.