Machine Learning
Matrix computations (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
A New Sammon Algorithm for Sparse Data Visualization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
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DNA Microarray technology allows us to monitor the expression level of thousands of genes simultaneously. This technique has become a relevant tool to identify different types of cancer. Several machine learning techniques such as the Support Vector Machines (SVM) have been proposed to this aim. However, common SVM algorithms are based on Euclidean distances which do not reflect accurately the proximities among the sample profiles. The SVM has been extended to work with non-Euclidean dissimilarities. However, no dissimilarity can be considered superior to the others because each one reflects different features of the data. In this paper, we propose to combine several Support Vector Machines that are based on different dissimilarities to improve the performance of classifiers based on a single measure. The experimental results suggest that our method reduces the misclassification errors of classifiers based on a single dissimilarity and a widely used combination strategy such as Bagging.