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http://pages.stat.wisc.edu/~wahba/ftp1/lee.lin.wahba.04.pdf
Multicategory Support Vector Machines: Theory and Application to the Classi” cation of Microarray Data and Satellite Radiance Data YoonkyungLEE,YiLIN,andGraceWAHBA Two-category support vector machines (SVM) have been very popular in the machine learning community for classi” cation problems.
http://proceedings.mlr.press/v2/liu07b/liu07b.pdf
Fisher Consistency of Multicategory Support Vector Machines Yufeng Liu Department of Statistics and Operations Research Carolina Center for Genome Sciences University of North Carolina Chapel Hill, NC 27599-3260 y°[email protected] Abstract The Support Vector Machine (SVM) has become one of the most popular ma-chine learning techniques in ...Cited by: 76
http://pages.stat.wisc.edu/~myuan/papers/rsvm.final.pdf
Reinforced Multicategory Support Vector Machines Yufeng L IU and Ming YUAN Support vector machines are one of the most popular machine learning methods for classification. Despite its great success, the SVM was originally designed for binary classification. Extensions to the multicategory case are important for general classifica-tion problems.
https://people.eecs.berkeley.edu/~jordan/papers/zhang-uai06.pdf
vector of 1’s, let Im denote the m m identity matrix, and let 0 denote the zero vector (or matrix) whose dimensionality is dependent upon the context. In ad-dition, A B represents the Kronecker product of A and B. 2.1 Multicategory Support Vector Machines The MSVM (Lee et al., 2004) is based on a c-tuple
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120762/
The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions ...Cited by: 7
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1780126/
Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive ...Cited by: 14
https://pdfs.semanticscholar.org/2a0e/2a249e34dc6de601a7f39204866e61ec2f78.pdf
Multicategory Support Vector Machines Yoonkyung Lee, Yi Lin, & Grace Wahba∗ Department of Statistics University of Wisconsin-Madison yklee,yilin,[email protected] Abstract The Support Vector Machine (SVM) has shown great performance in prac-tice as a classification methodology. Oftentimes multicategory problems have
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
http://www3.stat.sinica.edu.tw/statistica/oldpdf/a16n215.pdf
MULTI-CATEGORY SUPPORT VECTOR MACHINES, FEATURE SELECTION AND SOLUTION PATH Lifeng Wang and Xiaotong Shen University of Minnesota Abstract: Support Vector Machines (SVMs) have proven to deliver high perfor-mance. However, problems remain with respect to feature selection in multi-category classi cation.
https://amstat.tandfonline.com/doi/abs/10.1198/016214504000000098
Jan 20, 2017 · Two-category support vector machines (SVM) have been very popular in the machine learning community for classification problems. Solving multicategory problems by a series of binary classifiers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the …Cited by: 904
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