Support vector machine classifiers are widely used in pattern recognition applications. Contextual information can improve the classifier accuracy for image classification. The autocorrelation function can be used to estimate how relevant the neighborhood information is for a pixel classification. This paper proposes a support vector machine classifier that uses contextual information of the di...

Corporate credit rating is a process to classify commercial enterprises based on their creditworthiness. Machine learning algorithms can construct classification models, but in general they do not tend to be 100% accurate. Since they can be used as decision support for experts, interpretable models are desirable. Unfortunately, interpretable models are provided by only few machine learners. Fur...

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...

This paper presents a method of extending Support Vector Machines (SVMs) for dealing with multiclass problems. Motivated by the Decision Directed Acyclic Graph (DDAG), we propose the Adaptive DAG (ADAG): a modified structure of the DDAG that has a lower number of decision levels and reduces the dependency on the sequence of nodes. Thus, the ADAG improves the accuracy of the DDAG while maintaini...

structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. biggest class of the repetitive subsequences is “transposable elements” which has its own sub-classes upon contexts’ structures. many researches have been performed to criticality determine the structure and function of repetitive su...

Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image classification to handle nonlinearities. To classify an image at test time, an SVM requires matching it agains...

Winner-take-all multiclass classifiers are built on the top of a set of prototypes each representing one of the available classes. A pattern is then classified with the label associated to the most ‘similar’ prototype. Recent proposal of SVM extensions to multiclass can be considered instances of the same strategy with one prototype per class. The multi-prototype SVM proposed in this paper exte...

The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to a new parallel scenario. Graphics Processing Units (GPU) have arisen as high performance platforms to implement data parallel algorithms. In thi...

Support vector machine (SVM) which was originally designed for binary classification has achieved superior performance in various classification problems. In order to extend it to multiclass classification, one popular approach is to consider the problem as a collection of binary classification problems. Majority voting or winner-takes-all is then applied to combine those outputs, but it often ...

Support vector machine (SVM) is a kernel based novel pattern classification method that is significant in many areas like data mining and machine learning. A unique strength is the use of kernel function to map the data into a higher dimensional feature space. In training SVM, kernels and its parameters have very vital role for classification accuracy. Therefore, a suitable