کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
478690 1446126 2010 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning the optimal kernel for Fisher discriminant analysis via second order cone programming
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Learning the optimal kernel for Fisher discriminant analysis via second order cone programming
چکیده انگلیسی

Kernel Fisher discriminant analysis (KFDA) is a popular classification technique which requires the user to predefine an appropriate kernel. Since the performance of KFDA depends on the choice of the kernel, the problem of kernel selection becomes very important. In this paper we treat the kernel selection problem as an optimization problem over the convex set of finitely many basic kernels, and formulate it as a second order cone programming (SOCP) problem. This formulation seems to be promising because the resulting SOCP can be efficiently solved by employing interior point methods. The efficacy of the optimal kernel, selected from a given convex set of basic kernels, is demonstrated on UCI machine learning benchmark datasets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 203, Issue 3, 16 June 2010, Pages 692–697
نویسندگان
, , ,