Multiple kernel learning: Difference between revisions

Revision as of 12:16, 23 October 2017

Revision as of 00:55, 3 November 2017

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Fixed rules approaches such as the linear combination algorithm described above use rules to set the combination of the kernels. These do not require parameterization and use rules like summation and multiplication to combine the kernels. The weighting is learned in the algorithm. Other examples of fixed rules include pairwise kernels, which are of the form

:<math>k((x_{1i}, x_{1j}),(x_{2i},x_{2j}))=k(x_{1i},x_{2i})k(x_{1j},x_{2j})+k(x_{1i},x_{2j})k(x_{1j},x_{2i})</math>.

These pairwise approaches have been used in predicting protein-protein interactions.<ref>Ben-Hur, A. and Noble W.S. [httphttps://www.ncbi.nlm.nih.gov/pubmed/15961482?dopt=Abstract Kernel methods for predicting protein-protein interactions.] Bioinformatics. 2005 Jun;21 Suppl 1:i38-46.</ref>

====Heuristic approaches====