Support Vector Machines (SVM) for classification are being paid special attention in a number of practical applications. When using nonlinear Mercer kernels, the mapping of the input space to a highdimensional feature space makes the input feature selection a difficult task to be addressed. In this paper, we propose the use of nonparametric bootstrap resampling technique to provide with a stati...

We investigated the Classification of satellite images and multispectral remote sensing data .we focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area and green area. We carried out classification in three modules namely (a) Preprocess...

Two-level inverter control with type-1 and type-2 fuzzy logic-based space vector pulse-width modulation (PWM) method for induction motor drive (IMD) is presented in this paper. A new sampling time independent strategy based methods are used generating three phase duty ratios which directly obtained without mathematical equations. The conventional of (SVM) produces the dependent. However, PWM al...

The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition problems: 5 Thai tones, and 12 Thai vo...

Support Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity [6]. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems [9, 11]. In this paper, one of the first applications of Support Vector Machines (SVM) technique for the problem of...

We introduce SparseMinOver, a maximum margin Perceptron training algorithm based on the MinOver algorithm that can be used for SVM training when the feature vectors are sparse, high-dimensional, and binary. Such feature vectors arise when the CRO feature map is used to map the input space to the feature space. We show that the training algorithm is efficient with this type of feature vector, wh...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

In this paper, a new learning method is proposed to build Support Vector Machines (SVM) Binary Decision Function (BDF) of reduced complexity, efficient generalization and using an adapted hybrid color space. The aim is to build a fast and efficient SVM classifier of pixels. The Vector Quantization (VQ) is used in our learning method to simplify the training set. This simplification step maps pi...

In this paper we solve a document classification task by incorporating prior/domain knowledge onto the SVM. The algorithm consists in to learn a prior classifier in the primal space (words) from an ‘external’ source of information to the text classification itself: patterns of reader’s eyes movements when reading relevant words for discriminating texts. This prior weight vector is then plugged ...

The integration of a Z-source network with 5-Level three-phase inverter based cascaded to provide voltage step-up function is proposed in this paper. system controlling by an improved space vector modulation (SVM) the implantation algorithm and innovative virtual automated solutions can be considered, very fast simple. main objective achieve output twice applied input voltage, eliminate largest...