This paper describes experiments carried out at NiCT for the TREC 2009 Entity Ranking track. Our main study is to develop an effective approach to rank entities via measuring the “similarities” between supporting snippets of entities and input query. Three models are implemented to this end. 1) The DLM regards entity ranking as a task of calculating the probabilities of generating input query g...

Protein sequence motifs are gathering progressively attention in the sequence analysis area. The conserved regions have the potential to determine the conformation, function and activities of the proteins. We develop a new method combines the concept of granular computing and the power of Ranking-SVM to further extract protein sequence motif information generated from the FGK model. The quality...

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...

With the rapid development of Internet and E-commerce, the quantity of product reviews on the web grows very fast, but the review quality is inconsistent. This paper addresses the problem of automatically ranking reviews. A specification for judging the reviews quality is first defined and thus ranking review is formalized as ordinal regression problem. In this paper, we employ Ranking SVM as t...

The slope stability analysis is routinely performed by engineers to estimate the stability of river training works, road embankments, embankment dams, excavations and retaining walls. This paper presents a new approach to build a model for the prediction of slope stability state. The support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can so...

Support vector machine (SVM) is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM) and Threshold Accepting (TA-SVM) to determine the optimal value parameter and the ...

Support Vector Machines(SVMs) have succeeded in many classification fields. Some researchers have tried to apply SVMs to Intrusion Detection recently and got desirable results. By analyzing C-SVM theoretically and experimentally, we found that C-SVM had some properties which showed C-SVM was not most suitable for Network Intrusion Detection. First, C-SVM has different classification error rates...

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...