Multiple Kernel Support Vector Machine Problem Is NP-Complete

2014 ◽  
pp. 152-159 ◽  
Author(s):  
Luis Carlos Padierna ◽  
Juan Martín Carpio ◽  
María del Rosario Baltazar ◽  
Héctor José Puga ◽  
Héctor Joaquín Fraire
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine ◽  
Np Complete ◽  
Machine Problem

Bioprocess Soft Sensing Based on Multiple Kernel Support Vector Machine

2010 ◽  
Vol 108-111 ◽  
pp. 129-134
Author(s):  
Zhi Yong Du ◽  
Xian Fang Wang ◽  
Hai Yan Zhang
Keyword(s):  
Support Vector Machine ◽  
Average Molecular Weight ◽  
Mathematical Formulation ◽  
Multiple Kernel Learning ◽  
Support Vector ◽  
General Interest ◽  
Soft Sensing ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine ◽  
Productive Process

Soft sensing technology is one of the topics of general interest in study on current process control, which has recently drawn considerable attention worldwide, and has stimulated researchers and engineers to make greater effort to reduce the cost/benefit-ratio for development and manufacture of bio-industrial processes both economically and environmentally. This paper introduced a kind of soft-sensor based on an improved support vector machine (SVM) for a polyacrylonitrile productive process. The improved SVM called the multiple kernel support vector machine was presented, and the mathematical formulation of multiple kernel learning is given. Through the implementation for average molecular weight in polyacrylonitrile productive process, it demonstrates the good performance of the proposed method compared to single kernel.

Identification of DNA-Binding Proteins by Multiple Kernel Support Vector Machine and Sequence Information

2020 ◽  
Vol 17 (4) ◽  
pp. 302-310
Author(s):  
Yijie Ding ◽  
Feng Chen ◽  
Xiaoyi Guo ◽  
Jijun Tang ◽  
Hongjie Wu
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Computational Method ◽  
Support Vector ◽  
Sequence Information ◽  
Data Sets ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine

Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive. Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information. Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins. Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets. Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

scholarly journals IMPLEMENTASI METODE MULTIPLE KERNEL SUPPORT VECTOR MACHINE UNTUK SELEKSI FITUR DARI DATA EKSPRESI GEN DENGAN STUDI KASUS LEUKIMIA DAN TUMOR USUS BESAR

MATICS ◽  
2012 ◽  
Author(s):  
Ariana Yunita ◽  
Chastine Fatichah ◽  
Umi Laily Yuhana
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine

<p>Pada penelitian ini mengimplementasikan metode multiple kernel support vector machine untuk seleksi fitur. Multiple kernel merupakan metode modifikasi fungsi kernel yang mengalikan tiap elemen dari data. Metode ini melakukan seleksi fitur terhadap fitur yang kurang penting dengan tingkat akurasi lebih baik daripada metode dasar support vector machine. Uji coba dilakukan dengan menggunakan dataset ekspresi gen leukimia dan tumor usus besar. Hasil uji coba dibandingkan dengan tingkat akurasi metode support vector machine tanpa seleksi fitur. Tingkat akurasi metode multiple kernel support vector machine yang dihasilkan untuk data ekspresi gen leukimia yaitu 85% dan untuk data tumor usus besar sebesar 69%. Sedangkan tingkat akurasi dengan metode dasar support vector machine yaiu sebesar 82% untuk data leukimia dan 59% untuk data tumor usus besar. Seleksi fitur dapat mempersingkat waktu komputasi sehingga dapat dikembangkan untuk banyak aplikasi pengenalan pola.</p><p> </p><p><strong>Kata Kunci: </strong><em>Multiple kernel, support vector  machine</em>, seleksi fitur, data ekspresi gen</p>

Packet Matching Based on Multiple Kernel Support Vector Machine

2014 ◽  
Vol 12 (2) ◽  
pp. 294-299
Author(s):  
Wang Zelin ◽  
Wu Zhijian ◽  
Deng Changshou
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine
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