To segment a image with strongly varying object sizes results generally in under-segmentation of small structures or over-segmentation of big ones, which consequences poor classification accuracies. A strategy to produce multiple segmentations of one image and classification with support vector machines (SVM) of this segmentation stack afterwards is shown.

objective: interest in the subject of creativity and its impacts on human life is growing extensively. however, only a few surveys pay attention to the relation between creativity and physiological changes. this paper presents a novel approach to distinguish between creativity states from electrocardiogram signals. nineteen linear and nonlinear features of the cardiac signal were extracted to d...

We rederive a form of Joachims’ ξα method for tuning Support Vector Machines by the same approach as was used to derive the GACV, and show how the two methods are related. We generalize the ξα method to the nonstandard case of nonrepresentative training set and unequal misclassification costs and compare the result to the GACV estimate for the standard and nonstandard cases.

Using Support Vector Machines for MiRNA Identification

accurate protein function prediction is an important subject in bioinformatics, especially wheresequentially and structurally similar proteins have different functions. malate dehydrogenaseand l-lactate dehydrogenase are two evolutionary related enzymes, which exist in a widevariety of organisms. these enzymes are sequentially and structurally similar and sharecommon active site residues, spati...

In this chapter, we discuss two possible ways of improving the performance of the SVM, using geometric methods. The first adapts the kernel by magnifying the Riemannian metric in the neighborhood of the boundary, thereby increasing separation between the classes. The second method is concerned with optimal location of the separating boundary, given that the distributions of data on either side ...