Proceedings ArticleDOI
Emphatic Constraints Support Vector Machines for Multi-class Classification
Mostafa Sabzekar,Mahmoud Naghibzadeh,Hadi Sadoghi Yazdi,Sohrab Effati +3 more
- pp 118-123
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TLDR
First, the Emphatic Constraints Support Vector Machines (ECSVM) is proposed as a new powerful classification method and extended to find efficient multi-class classifiers, and the obtained results show the superiority of the method.Abstract:
—Support vector machine (SVM) formulation has been originally developed for binary classification problems. Finding the direct formulation for multi-class case is not easy but still an on-going research issue. This paper presents a novel approach for multi-class SVM by modifying the training phase of the SVM. First, we propose the Emphatic Constraints Support Vector Machines (ECSVM) as a new powerful classification method. Then, we extend our method to find efficient multi-class classifiers. We evaluate the performance of the proposed scheme by means of real world data sets. The obtained results show the superiority of our method.read more
Citations
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Journal ArticleDOI
A noise-aware feature selection approach for classification
Mostafa Sabzekar,Zafer Aydin +1 more
TL;DR: In this article, a noise-aware version of support vector machines (SVM) is utilized for feature selection and a new algorithm for removing irrelevant features is proposed by combining this method and sequential backward search (SBS).
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI
Solving multiclass learning problems via error-correcting output codes
TL;DR: In this article, error-correcting output codes are employed as a distributed output representation to improve the performance of decision-tree algorithms for multiclass learning problems, such as C4.5 and CART.
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