Add to ORKGAbstract A unified view on multi-class support vector machines (SVMs) is presented, covering most prominent variants including the one-vs-all approach and the algorithms proposed by We- We analyze Fisher consistency of multi-class loss functions and universal consistency of the various machines. On the one hand, we give examples of SVMs that are, in a particular hyperparameter regime, universally consistent without being based on a Fisher consistent loss. These include the canonical extension of SVMs to multiple classes as proposed by Weston & Watkins and Vapnik as well as the one-vs-all approach. On the other hand, it is demonstrated that machines based on Fisher consistent loss functions can fail to identify proper decision boundaries...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
In SVMs-based multiple classification, it is not always possible to find an appropriate kernel funct...
Support vector machines (SVM) were originally designed for binary classification. How to effectively...
Lately, Support Vector Machine (SVM) methods have become a very popular technique in the machine le...
Support Vector Machines (SVMs) are excellent candidate solutions to solving multi-class problems, an...
In this paper we have studied the concept and need of Multiclass classification in scientific resear...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-li...
. The solution of binary classification problems using support vector machines (SVMs) is well develo...
Support Vector Machine (SVM) was first proposed by Cortes and Vapnik in 1995. It is developed from t...
Support Vectors (SV) are a machine learning procedure based on Vapnik’s Statistical Learning Theory,...
We present new decomposition algorithms for training multi-class support vector machines (SVMs), in ...
In this dissertation, we study the multi-category support vector machines (k-SVM). The design of the...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
In SVMs-based multiple classification, it is not always possible to find an appropriate kernel funct...
Support vector machines (SVM) were originally designed for binary classification. How to effectively...
Lately, Support Vector Machine (SVM) methods have become a very popular technique in the machine le...
Support Vector Machines (SVMs) are excellent candidate solutions to solving multi-class problems, an...
In this paper we have studied the concept and need of Multiclass classification in scientific resear...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
Support Vector Machine (SVM) is a binary classifier, but most of the problems we find in the real-li...
. The solution of binary classification problems using support vector machines (SVMs) is well develo...
Support Vector Machine (SVM) was first proposed by Cortes and Vapnik in 1995. It is developed from t...
Support Vectors (SV) are a machine learning procedure based on Vapnik’s Statistical Learning Theory,...
We present new decomposition algorithms for training multi-class support vector machines (SVMs), in ...
In this dissertation, we study the multi-category support vector machines (k-SVM). The design of the...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
In SVMs-based multiple classification, it is not always possible to find an appropriate kernel funct...