کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
402749 676998 2013 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Two-stage multiple kernel learning with multiclass kernel polarization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Two-stage multiple kernel learning with multiclass kernel polarization
چکیده انگلیسی

The success of kernel methods is very much dependent on the choice of kernels. Multiple kernel learning (MKL) aims at learning a combination of different kernels in order to better match the underlying problem instead of using a single fixed kernel. In this paper, we propose a simple but effective multiclass MKL method by a two-stage strategy, in which the first stage finds the kernel weights to combine the kernels, and the second stage trains a standard multiclass support vector machine (SVM). Specifically, we first present an evaluation criterion named multiclass kernel polarization (MKP) to assess the quality of a kernel in the multiclass classification scenario, and then develop a heuristic rule to directly assign a weight to each kernel based on the quality of the individual kernel. MKP is a multiclass extension of the kernel polarization, which is a universal kernel evaluation criterion for kernel design and learning. Comprehensive experiments are conducted on several UCI benchmark examples and the results well demonstrate the effectiveness and efficiency of our approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 48, August 2013, Pages 10–16
نویسندگان
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