We explore a technique to learn Support Vector Models (SVMs) when training data is partitioned among several data sources. The basic idea is to consider SVMs which can be reduced to Minimal Enclosing Ball (MEB) problems in an feature space. Computation of such SVMs can be efficiently achieved by finding a coreset for the image of the data in the feature space. Our main result is that the union ...

Space Vector Modulation (SVM) based Direct Torque Control (DTC) scheme is used for a variable –speed direct drive Permanent Magnet Synchronous Generator (PMSG) in a wind power generation system. SVM Provides more flexibility for inverter voltage utilization, in order to compensate the torque and flux errors in a smoother way than conventional DTC. SVM based DTC maintains constant switching freq...

MRI is the most important technique, in detecting the brain tumor. In this paper data mining methods are used for classification of MRI images. A new hybrid technique based on the support vector machine (SVM) and fuzzy c-means for brain tumor classification is proposed. The purposed algorithm is a combination of support vector machine (SVM) and fuzzy c-means, a hybrid technique for prediction o...

Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of the C-SVM and the ν-SVM, which we denote the 2...

Recent research has demonstrated the merit of combining Gaussian mixture models and support-vector-machine (SVM) for text-independent speaker verification. However, one unaddressed issue in this GMM–SVM approach is the imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM. This paper proposes a resampling technique...

The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...

The relevance feedback approach is a powerful technique in content-based image retrieval (CBIR) tasks. In past years, many intraquery learning techniques have been proposed to solve the relevance feedback problem. Among these techniques, Support Vector Machines (SVM) have shown promising results in the area. More specifically, in relevance feedback applications the SVMs are typically been used ...

Abstract This paper proposes a voltage and frequency controller for a wind turbine-driven independent asynchronous generator. The proposed controller consists of IGBT (Insulated Gate Bipolar Transistor) based three phase voltage source converter along with battery energy storage system at its DC bus and it inherently adapts to the changes in the rotor speed while maintaining a near constant vol...