Millions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and int...

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on Deutsche Bundesbank data. In particular we discuss the selection of variables...

Large margin classifiers have proven to be effective in delivering high predictive accuracy, particularly those focusing on the decision boundaries and bypassing the requirement of estimating the class probability given input for discrimination. As a result, these classifiers may not directly yield an estimated class probability, which is of interest itself. To overcome this difficulty, this ar...

One-class support vector machine is an important and efficient classifier which is used in the situation that only one class of data is available, and the other is too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, there is reason to consider data as tensor. Tensor representation can make use of the s...

Support vector machine (SVM) and HMAX model are two powerful recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper we combine these two techniques for the palmprint v...

This paper proposes the implementation of Support Vector Machine (SVM) on a single chip (dsPIC), which makes it suitable for standalone portable applications. The usual practice is to implement SVMs using general purpose computers, since its implementation demands fairly large amount of memory [1]. SVM implementation in this work uses Sequential Minimal Optimization (SMO) [1], with necessary mo...