This paper proposes a mathematical programming framew ork for combining SVMs with possibly di erent kernels. Compared to single SVMs, the advantage of this approach is tw ofold: it creates SVMs with local domains of expertise leading to local enlargements of the margin, and it allows the use of simple linear kernels combined with a xed boolean operation that is particularly well suited for buil...

Support Vector Machines (SVMs) are state-of-the-art algorithms for classification in machine learning. However, the SVM formulation does not directly seek to find sparse solutions. In this work, we propose an alternate formulation that explicitly imposes sparsity. We show that the proposed technique is related to the standard SVM formulation and therefore shares similar theoretical guarantees. ...

The maximization of the performance of the most if not all the fuzzy identification techniques is usually expressed in terms of the generalization performance of the derived neuro-fuzzy construction. Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance. However, these SVFI rules usually lack...

Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Mac...

in the present paper, we introduce and study a fuzzy vector equilibrium problem and prove some existence results with and without convexity assumptions by using some particular forms of results of textit{kim} and textit{lee} [w.k. kim and k.h. lee, generalized fuzzy games and fuzzy equilibria, fuzzy sets and systems, 122 (2001), 293-301] and textit{tarafdar} [e. tarafdar, fixed point theorems i...

Fuzziness must be considered in systems where available information is uncertain. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM). This integration preserves the benefits of SV...

The design of a support vector machine with Gaussian kernels is considered for modeling nonlinear processes. The structure is equivalent to a neuro-fuzzy system based on radial basis function network considering some restrictions. To improve the interpretability and reduce the complexity of the structure a hybrid learning scheme is proposed. First, the input-output data is supervised clustered ...

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...