A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. In this paper, we present an improved version of the method, called editing sup...

Discrete support vector machines (DSVM), recently proposed in [l01 and [ l l ] for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing a one-against-all framework in which a set of binary discrimination problems are solved by means of DSVM. Comput...

In this paper, we present a high accurate system for FrameNet semantic role classification based on the innovative features derived from a combined use of FrameNet, VerbNet and PropBank. The main property of our approach is a unified view of the above three resources which is theoretically supported by the linking theory. Experiments on Support Vector Machines (SVM) show that our system classif...

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A two pronged strategy, one involving the Support Vector Machine (SVM) as the classifier and the other including physicochemical properties as additional features, is proposed and implemented here for improved prediction of multi-domains in protein chains. It is experimentally observed to have achieved an accuracy of 76.46 after 25 fold cross validation of results on curated data, derived from ...

Due to its various negative consequences impervious surface is increasingly recognized as a key issue in assessing the sustainability of land use – particularly in urban environments. In Germany, a regional or state-wide method for an accurate, fast and cost-effective quantification and assessment of impervious surfaces is still missing. Thus, we researched into the characterization of impervio...

In recent years, the supply chain managements have been paid more and more attention. The supply chain risk management is an important content for enterprises implementing supply chain management. Therefore, how to measure the risk of supply chain is quite important. In this study, a supply chain risk evaluation model based on support vector machines and two-dimensional risk matrix is proposed....

This paper uses the supervised learning techniques of linear regression and support vector machines in an attempt to predict the merging behavior of drivers on U.S. Highway 101 based on current traffic patterns. Using highway data from the Federal Highway Administration, the paper approaches the problem from numerous angles, eventually concluding that using a twostep approach achieves the best ...