Kernel covering algorithm combined the kernel function algorithm of SVM and the covering algorithm of constructive machine learning method, was applied in the forecast shot-time load. Constituting the kernel function based SVM, selecting the sample according to load day property, history temperature data and history load data. The algorithm achieved separating the sample set automatically and f...

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, we present a kernel-based fuzzy clustering algorithm that exploits the spatial contextual information in image data. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm using a kernel-in...

The accuracy levels achieved by state-of-the-art Speaker Verification systems are high enough for the technology to be used in real-life applications. Unfortunately, the transfer from the lab to the field is not as straight-forward as could be: the best performing systems can be computationally expensive to run and need large speaker model footprints. In this paper, we compare two speaker verif...

Laser ultrasonic defect detection and classification has been widely used in engineering and material defect detection, so detecting and classifying the defect targets accurately is significant. In order to obtain the higher classification accuracy, an improved support vector machine (SVM) based on particle swarm optimization algorithm is used as classifier in this paper. To search the optimal ...

Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decod...

EU-IST Integrated Project (IP) IST-2003-506826 SEKT Deliverable D2.1.1 (WP2) This deliverable presents an SVM-based algorithm for IE and experiments on several benchmark datasets. The results showed that our system is comparable to other state-of-the-art systems on both traditional IE and adaptive IE tasks. We investigated two feature weighting schemes, the impact of different NLP features on t...

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...

The main objective of the current work was to enhance transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical thermal classes were categorized IEC IEEE standard rules. concentration five combustible gases (hydrogen, methane, ethane, ethylene, acetylene...

Distribution of data and manipulation allows for solving larger problems and executing applications that are distributed in nature. In this paper we present a grid-based distributed Support Vector Machine (SVM) algorithm. The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, in situations and resource...

Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the strengths from multiple sources. This paper presents concrete cooperation between automatic algorithms, interactive algorithms and visualization tools. The first kind of cooperation is an interactive decision tree algorithm called CIAD+. It allow...