This paper explores utilization of information from social networks in making automatic movie recommendations. Implementations of three different algorithms (SVM, Clustering, and Ranking SVM) are implemented and evaluated. The general approach utilizes a large collection of Facebook profile information as training set in order to generate a list of movie recommendations for a particular user (c...

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...

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...

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...

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...

In this research, we exploit the regularize framework and proposed an associative classification algorithm for uncertain data. The major recompense of SVM(support vector machine) are: recurrent item sets capture every dominant associations between items in a dataset. These classifiers naturally handle missing values and outliers as they only deal with statistically significant associations whic...

The radiosity method is a very demanding process in terms of computing and memory resources. To cope with these problems, parallel solutions have been proposed in the literature. These solutions are outlined and classiied in this paper. A new parallel solution, based on the use of a shared virtual memory (SVM), is proposed. It will be shown that this concept of SVM greatly simpliies the impleme...

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...