matterhacker

Another SVMs applied to DNA microarray data project.  Looks like it’s from a collaboration called MLExAI between the University of Hartford and Central Connecticut State University that is an information exchange on teaching machine learning and AI material.

A really good (but basic) intro on Support Vector Machines from Nature Biotechnology written by William Noble. Explains the basics of SVMs using DNA Microarray data analysis. From the article:

“…to understand the essence of SVM classification, one needs only to grasp four basic concepts: (i) the separating hyperplane, (ii) the maximum-margin hyperplane, (iii) the soft margin and (iv) the kernel function.”

Good read for a conceptual understanding… but no math.