The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognitio
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A statistical framework for genomic data fusion
Bioinformatics
Cost-conscious classifier ensembles
Pattern Recognition Letters
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Canonical correlation analysis using within-class coupling
Pattern Recognition Letters
Multi kernel learning with online-batch optimization
The Journal of Machine Learning Research
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Recently, it has been proposed to combine multiple kernels using a weighted linear sum. In certain applications, different kernels may be using different input representations and these methods do not consider neither the cost of acquiring them nor the cost of evaluating the kernels. We generalize the framework of Multiple Kernel Learning (Mkl) for this cost-conscious methodology. On 12 benchmark data sets from the UCI repository, we compare Mkl and its cost-conscious variants in terms of accuracy, support vector count, and total cost. Cost-conscious Mkl achieves statistically similar accuracy results by using fewer support vectors/kernels by best trading off accuracy brought by each representation/kernel with the concomitant cost. We also test our approach on two popular bioinformatics data sets from MIPS comprehensive yeast genome database (CYGD) and see that integrating the cost factor into kernel combination allows us to obtain cheaper kernel combinations by using fewer active kernels and/or support vectors.