This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as “online RK-OC-ELM”. The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC). Further, the basic hyper plane model is adapted in an online fashio...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.03.023 q This project was supported by CONACyT unde scholarship 205834. ⇑ Corresponding author. Tel.: +52 2222663100x831 E-mail address: [email protected] (H.J. Escalant This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS c...

In this paper we propose a new type of distance-based classifier. Traditionally, these classifiers are instancebased: they classify a test instance by computation of a similarity measure between that instance and the instances in the training-set and assigning it the same class of the most similar k instances. This method is simple but has some disadvantages, among which there is the greater se...

The Receiver Operating Characteristic (ROC) has become a standard tool for the analysis and comparision of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we discuss and present a number of different extensions to the standard two-class ROC for multi-class problems. We define the ROC surfa...

We study PAC learning in the presence of strategic manipulation, where data points may modify their features certain predefined ways order to receive a better outcome. show that vanilla ERM principle fails achieve any nontrivial guarantee this context. Instead, we propose an incentive-aware version which has asymptotically optimal sample complexity. then focus our attention on incentive-compati...

In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a...

It has been demonstrated in the literature that the combining of different biometric traits is a powerful tool to overcome the limitations imposed by a single biometric system. The fusion of different systems can be approached in different ways. In this work, we consider the pattern classification approach, where the scores of the various systems are used as features to feed the classifiers. Mo...

In this paper we address the problem of skewed class distribution in implicit discourse relation recognition. We examine the performance of classifiers for both binary classification predicting if a particular relation holds or not and for multi-class prediction. We review prior work to point out that the problem has been addressed differently for the binary and multi-class problems. We demonst...

Learning classifiers from imbalanced or skewed datasets is an important topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of ...