This paper describes a novel application of support vector machines and multiscale texture and color invariants to a problem in biological oceanography: the identification of 6 sp...
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
We describe SVMotif, a support vector machine-based learning algorithm for identification of cellular DNA transcription factor (TF) motifs extrapolated from known TF-gene interact...
Mark A. Kon, Yue Fan, Dustin T. Holloway, Charles ...
We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the...
Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint ...