Space vector pulse width modulation (SVPWM) is a superior switching technique offering several benefits for power electronic inverters. However, concerning multilevel inverters (MLIs), implementing SVPWM demanding and time-consuming task because it deals with the six sectors of space (SVM) plane numerous regions vectors. Several research works in literature tried to simplify implementation MLIs...

In this paper, we present our method which is a performance improvement to the Directed Acyclic Graph Support Vector Machines (DAG SVM). It suggests a weighted multi-class classification technique which divides the input space into several subspaces. In the training phase of the technique, for each subspace, a DAG SVM is trained and its probability density function (pdf) is guesstimated. In the...

Hardware complexity of a motor drive can be reduced by estimating controls rather than measuring using sensors. In this way the corresponding sensors are eliminated and the system becomes more robust. This article is about speed control of a sensor less induction motor with predictive current controller technique. This particular speed control technique does not require direct measurements of m...

We use kernel Canonical Correlation Analysis to learn a semantic representation of Web images and their associated text. This representation is used in two applications. In first application we consider classification of images into one of three categories. We use SVM in the semantic space and compare against the SVM on raw data and against previously published results using ICA. In the second ...

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily C...

Support Vector Machine (SVM) is a fundamental technique in machine learning. A long time challenge facing SVM is how to deal with outliers (caused by mislabeling), as they could make the classes in SVM nonseparable. Existing techniques, such as soft margin SVM, ν-SVM, and Core-SVM, can alleviate the problem to certain extent, but cannot completely resolve the issue. Recently, there are also tec...

Dissimilarities are extremely useful in many real-world pattern classification problems, where the data resides in a complicated, complex space, and it can be very difficult, if not impossible, to find useful feature vector representations. In these cases a dissimilarity representation may be easier to come by. The goal of this work is to provide a new technique based on Support Vector Machines...