کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405231 677510 2013 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Random projection ensemble learning with multiple empirical kernels
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Random projection ensemble learning with multiple empirical kernels
چکیده انگلیسی

In this paper we propose an effective and efficient random projection ensemble classifier with multiple empirical kernels. For the proposed classifier, we first randomly select a subset from the whole training set and use the subset to construct multiple kernel matrices with different kernels. Then through adopting the eigendecomposition of each kernel matrix, we explicitly map each sample into a feature space and apply the transformed sample into our previous multiple kernel learning framework. Finally, we repeat the above random selection for multiple times and develop a voting ensemble classifier, which is named RPEMEKL. The contributions of the proposed RPEMEKL are: (1) efficiently reducing the computational cost for the eigendecomposition of the kernel matrix due to the smaller size of the kernel matrix; (2) effectively increasing the classification performance due to the diversity generated through different random selections of the subsets; (3) giving an alternative multiple kernel learning from the Empirical Kernel Mapping (EKM) viewpoint, which is different from the traditional Implicit Kernel Mapping (IKM) learning.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 37, January 2013, Pages 388–393
نویسندگان
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