This paper presents a strategy of adaptive online sketchy shape recognition. The inputting sketchy shapes are recognized online by means of a modified Support Vector Machine (SVM) incremental learning classifier. All classified results evaluated by user are collected and some important samples are selected according to their distances to the hyper-plane of the SVM-classifier. The classifier can...

This paper proposes a method for Urdu language text. Character recognition is obtained by OCR. This paper represents the effectiveness of characters with SVM Classifier using Hierarchical approach. SVM is a useful technique for data classification. The objective of SVM is to generate a model which predicts the target value. The work is done on Sindhi Character Set. The experiment shows that cha...

A class-imbalanced classifier is a decision rule to predict the class membership of new samples from an available data set where the class sizes differ considerably. When the class sizes are very different, most standard classification algorithms may favor the larger (majority) class resulting in poor accuracy in the minority class prediction. A class-imbalanced classifier typically modifies a ...

Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses ...

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the highprecision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of t...

The Support Vector Machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick [1– 3]. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. The SVM uses linear threshold elements to build up two-classes classifier. It learns linear threshold element pa...

In conventional method, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for dis...

This paper presents a new framework based on modified EMD method for detection of single and multiple PQ issues. In modified EMD, DWT precedes traditional EMD process. This scheme makes EMD better by eliminating the mode mixing problem. This is a two step algorithm; in the first step, input PQ signal is decomposed in low and high frequency components using DWT. In the second stage, the low freq...

In this paper, various cooperation schemes of SVM (Support Vector Machine) classifiers applied on two feature sets for handwritten digit recognition are examined. We start with a feature set composed of structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partitions of the feature set as well as the advantage...