The nu-support vector machine (nu-SVM) for classification proposed by Schölkopf, Smola, Williamson, and Bartlett (2000) has the advantage of using a parameter nu on controlling the number of support vectors. In this article, we investigate the relation between nu-SVM and C-SVM in detail. We show that in general they are two different problems with the same optimal solution set. Hence, we may ex...

Normal support vector machine (SVM) is not suitable for classification of large data sets because of high training complexity. Convex hull can simplify the SVM training. However, the classification accuracy becomes lower when there exist inseparable points. This paper introduces a novel method for SVM classification, called convex–concave hull SVM (CCH-SVM). After grid processing, the convex hu...

Support Vector Machine (SVM) is the state-of-art learning machine that has been very fruitful not only in pattern recognition, but also in data mining areas, such as feature selection on microarray data, novelty detection, the scalability of algorithms, etc. SVM has been extensively and successfully applied in feature selection for genetic diagnosis. In this paper, we do the contrary,i.e., we u...

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the comput...

We propose a novel classification framework called the videospecific SVM (V-SVM) for normal-vs-abnormal white-light colonoscopy image classification. V-SVM is an ensemble of linear SVMs, with each trained to separate the abnormal images in a particular video from all the normal images in all the videos. Since V-SVM is designed to capture lesion-specific properties as well as intra-class variati...

The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an ef£cient algorithm that computes the whole solution path of the 1-norm SVM, hence facilitates adaptive selection of...

Support vector machine (SVM) was used to analyze the occurrence of roach in Flemish stream basins (Belgium). Several habitat and physico?chemical variables were used as inputs for the model development. The biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. Genetic algorithm (GA) was combined with SVM in order to select the most important...

Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...

Ranking algorithms are often introduced with the aim of automatically personalising search results. However, most ranking algorithms developed in the machine learning community rely on a careful choice of some regularisation parameter. Building upon work on the regularisation path for kernel methods, we propose a parameter selection algorithm for ranking SVM. Empirical results are promising.

Inconsistencies are part of any manually annotated corpus. Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data. Past research has focused mainly on detecting inconsistency in syntactic annotation. This work explores new approaches to detecting inconsistency in semantic annotation. Two ranking methods are presented in this paper: a...