Add to ORKGEvolutionary support vector machines (ESVMs) are a novel technique that assimilates the learning engine of the state-of-the-art support vector machines (SVMs) but evolves the coefficients of the decision function by means of evo-lutionary algorithms (EAs). The new method has accom-plished the purpose for which it has been initially devel-oped, that of a simpler alternative to the canonical SVM approach for solving the optimization component of train-ing. ESVMs, as SVMs, are natural tools for primary ap-plication to classification. However, since the latter had been further on extended to also handle regression, it is the scope of this paper to present the corresponding evolution-ary paradigm. In particular, we consider the hybridization wit...
Support vector machines are relatively new approach for creating classifiers that have become increa...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
This chapter describes a Support Vector Machine (SVM) for default prediction that features evolution...
Abstract. The paper proposes a first attempt to approach regression from the perspective of the nove...
We propose a novel learning technique for classification as result of the hybridization between supp...
Abstract — Within the present paper, we put forward a novel hybridization between support vector mac...
Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled b...
Summary. The paper presents a novel, combined methodology to target parameter tuning. It uses Latin ...
Abstract. Support vector machines are a modern and very efficient learning heuristic. However, their...
From the beginning, machine learning methodology, which is the origin of artificial intelligence, ha...
Support vector machines (SVMs) were originally formulated for the solution of binary classification ...
We show that genetic algorithms (GA) find optimized dynamic support vector machines (DSVMs) more eff...
The foundations of Support Vector Machines (SVM) have been developed by Vapnik and are gaining popul...
This chapter describes a Support Vector Machine (SVM) for default prediction that features evolution...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline ...
Support vector machines are relatively new approach for creating classifiers that have become increa...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
This chapter describes a Support Vector Machine (SVM) for default prediction that features evolution...
Abstract. The paper proposes a first attempt to approach regression from the perspective of the nove...
We propose a novel learning technique for classification as result of the hybridization between supp...
Abstract — Within the present paper, we put forward a novel hybridization between support vector mac...
Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled b...
Summary. The paper presents a novel, combined methodology to target parameter tuning. It uses Latin ...
Abstract. Support vector machines are a modern and very efficient learning heuristic. However, their...
From the beginning, machine learning methodology, which is the origin of artificial intelligence, ha...
Support vector machines (SVMs) were originally formulated for the solution of binary classification ...
We show that genetic algorithms (GA) find optimized dynamic support vector machines (DSVMs) more eff...
The foundations of Support Vector Machines (SVM) have been developed by Vapnik and are gaining popul...
This chapter describes a Support Vector Machine (SVM) for default prediction that features evolution...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline ...
Support vector machines are relatively new approach for creating classifiers that have become increa...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
This chapter describes a Support Vector Machine (SVM) for default prediction that features evolution...