We consider regularized support vector machines (SVMs) and show that they are precisely equiva-lent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the equivalence suggests more general SVM-like algorithms for classification that explicitly build in protection to noise, and at the same time control overfitting. On the analysis front, the equiva-lence of robustness and regularization provides a robust optimization interpretation for the success of regularized SVMs. We use this new robustness interpretation of SVMs to give a new proof of consistency of (kernelized) SVMs, thus establishing robustness as ...
Support vector machine (SVM) is one of the most successful learning methods for solving classificatio...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
Support Vector Machines (SVMs) are known to be consistent and robust for classification and regressi...
We consider a robust classification problem and show that standard regularized SVM is a special case...
Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistic...
Previous analysis of binary support vector machines (SVMs) has demonstrated a deep connection betwee...
AbstractSupport vector machines (SVMs) have attracted much attention in theoretical and in applied s...
Abstract—We propose a new family of classification algorithms in the spirit of support vector machin...
AbstractSupport vector machines (SVMs) have attracted much attention in theoretical and in applied s...
The main objective of this work is to investigate the robustness and stability of the behavior of th...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support vector machine (SVM) is one of the most successful learning methods for solving classificatio...
Support vector machine (SVM) is one of the most successful learning methods for solving classificatio...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
Support Vector Machines (SVMs) are known to be consistent and robust for classification and regressi...
We consider a robust classification problem and show that standard regularized SVM is a special case...
Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistic...
Previous analysis of binary support vector machines (SVMs) has demonstrated a deep connection betwee...
AbstractSupport vector machines (SVMs) have attracted much attention in theoretical and in applied s...
Abstract—We propose a new family of classification algorithms in the spirit of support vector machin...
AbstractSupport vector machines (SVMs) have attracted much attention in theoretical and in applied s...
The main objective of this work is to investigate the robustness and stability of the behavior of th...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support Vector Machines (SVMs) are a group of supervised learning machines introduced by Vladimir V...
Support vector machine (SVM) is one of the most successful learning methods for solving classificatio...
Support vector machine (SVM) is one of the most successful learning methods for solving classificatio...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
Support Vector Machines (SVMs) are known to be consistent and robust for classification and regressi...