# Multicategory Support Vector Machines

@article{Lee2004MulticategorySV, title={Multicategory Support Vector Machines}, author={Yoonkyung Lee and Yi Lin and Grace Wahba}, journal={Journal of the American Statistical Association}, year={2004}, volume={99}, pages={67 - 81} }

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 binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when… Expand

#### 447 Citations

Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines

- Computer Science
- 2004

The proposed MSVM in addition provides a unifying framework when there are either equal or unequal misclassification costs, and when there is a possibly nonrepresentative training set. Expand

Ensemble Approaches of Support Vector Machines for Multiclass Classification

- Computer Science
- PReMI
- 2007

Two novel ensemble approaches are presented: probabilistic ordering of one-vs-rest (OVR) SVMs with naive Bayes classifier and multiple decision templates of OVR SVMs. Expand

A New Multiclass Support Vector Machine

- Computer Science
- 2012

A new multiclass Support Vector Machine (SVM) is presented, which can be used to find the optimal decision boundaries in a multiclass classification problem, and it is believed that the proposed method provides a promising new way of looking at multiclass Classification problems. Expand

The consistency of multicategory support vector machines

- Mathematics, Computer Science
- Adv. Comput. Math.
- 2006

The goal of classification is to construct a classifier with small misclassification error, and the consistency of MSVMs is established under a mild condition, where the universal consistency holds true if the reproducing kernel Hilbert space is dense in C norm. Expand

A New Multiclass Support Vector Machine An Approach Using Iterative Majorization and Huber Hinge Errors

- 2012

A new multiclass Support Vector Machine (SVM) is presented, which can be used to find the optimal decision boundaries in a multiclass classification problem. In the multiclass classification problem… Expand

Support vector machines: A distance-based approach to multi-class classification

- Mathematics
- 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET)
- 2016

One of the main tasks sought after with machine learning is classification. Support vector machines are one of the widely used machine learning algorithms for data classification. SVMs are by default… Expand

Support Vector Machines for Unbalanced Multicategory Classification

- Mathematics
- 2015

Classification is a very important research topic and its applications are various, because data can be easily obtained in these days. Among many techniques of classification the support vector… Expand

Reinforced Angle-Based Multicategory Support Vector Machines

- Mathematics, Medicine
- Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
- 2016

It is proved that RAMSVMs can enjoy Fisher consistency, and it is shown that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem, using an angle-based prediction rule with k − 1 functions directly. Expand

Multiclass Approaches for Support Vector Machine Based Land Cover Classification

- Computer Science
- ArXiv
- 2008

Results from this study conclude the usefulness of One vs. One multi class approach in term of accuracy and computational cost over other multi class approaches. Expand

Support Vector Machine Implementations for Classification & Clustering

- Computer Science, Medicine
- BMC Bioinformatics
- 2006

Benefits of theinternal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy are described. Expand

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