Classification is the grouping of objects according to their characteristics. Scientists use classification system to organize information into logically related chunks so that it will be easy to analyze and evaluate. There are various classification techniques ranging from simple techniques such as Rule based and Nearest-Neighbour classifiers to more advanced techniques such as Support Vector ...

This paper presents a scheme to identify the blur parameters using support vector machine (SVM) Multiclass approach has been used to classify the length of motion blur and sigma parameter of atmospheric blur. Different models of SVM have been constructed to classify the parameters. Experimental results show the robustness of the proposed approach to classify blur parameters.

In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our ...

We propose a method based on the support vector machine to assess a set of clinical parameters for MCG diagnoses. By using this method, we can conclude whether the parameters are suitable for classifying a specific disease, and we can extract the dominantly working parameters from the set. In this study, we test a well-known set of the field-map parameters for ischemia detection by using our me...

Driving is a complex task that requires constant attention, and intelligent transportation systems that support drivers in this task must continually infer driver intentions to produce reasonable, safe responses. In this paper we describe a technique for inferring driver intentions, specifically the intention to change lanes, using support vector machines (SVMs). The technique was applied to ex...

In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight distribution should be taken into account upon prediction, and it also alleviates the usual tendency of assigning overly high confidence to the estim...

In this paper, we introduce a SAR images target recognition approach based on the contourlet transform and support vector machine (SVM), which takes technological advantages of both SVM and the contourlet transform for feature extraction.