The support vector machine (SVM) learning algorithm and related techniques have recently gained widespread attention. SVMs have many mathematical features that make them attractive for genomic analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. In this talk, I will describe the application of SVM techiques to learning gene functional classifications from multiple types of genomic data, including microarray expression measurements and promoter region patterns.
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