Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least squ...

This paper describes our contribution to the English Entrance Exams task of CLEF 2015, which requires participating systems to automatically solve multiple choice reading comprehension tasks. We use a combination of text segmentation and different similarity measures with the aim of exploiting two observed aspects of tests: 1) the often linear relationship between reading text and test question...

The utilization of shared (P2P) applications is developing significantly, which brings about a few major issues, for example, the system clog and traffic obstruction. Consequently P2P traffic identification is the most blazing theme of P2P traffic administration. Support vector machine (SVM) has points of interest with settling little examples for P2P characterization issues. However, the execu...

Support vector machine (SVM) was used to analyze the occurrence of roach in Flemish stream basins (Belgium). Several habitat and physico?chemical variables were used as inputs for the model development. The biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. Genetic algorithm (GA) was combined with SVM in order to select the most important...

In this thesis we deal with two geometric optimization problems: Minimizing Absolute Gaussian Curvatue Locally in Meshes and the Solution path of the Slab Support Vector Machine. In the first part, we show that given a polygon C in R it is in general algebraically hard to find a point in R3\C at which the absolute Gaussian curvature with respect to C is minimized. By algebraically hard we mean ...

Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the e...

This paper describes an e-mail spam filter based on local SVM, namely on the SVM classifier trained only on a neighborhood of the message to be classified, and not on the whole training data available. Two problems are stated and solved. First, the selection of the right size of neighborhood is shown to be critical; our solution is based on the estimation of the a-posteriori probability of the ...