Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. A common approach to road detection consists of exploiting color features to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. ...

This paper introduces the Graph Embedded One-Class Support Vector Machine and Graph Embedded Support Vector Data Description methods. These methods constitute novel extensions of the One-Class Support Vectors Machines and Support Vector Data Description, incorporating generic graph structures that express geometric data relationships of interest in their optimization process. Local or global re...

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...

One-class classification approaches have been proposed in the literature to learn classifiers from examples of only one class. But these approaches are not directly applicable to relational domains due to their reliance on a feature vector or a distance measure. We propose a nonparametric relational one-class classification approach based on first-order trees. We learn a tree-based distance mea...

We present an information theoretic framework for one-class classification, which allows for deriving several new novelty scores. With these scores, we are able to rank samples according to their novelty and to detect outliers not belonging to a learnt data distribution. The key idea of our approach is to measure the impact of a test sample on the previously learnt model. This is carried out in...

When the number of objects in the training set is too small for the number of features used, most classification procedures cannot find good classification boundaries. In this paper, we introduce a new technique to solve the one class classification problem based on fitting an implicit polynomial surface to the point cloud of features to model the one class which we are trying to separate from ...

Support vector machines (SVMs) have gained great attention and have been used extensively and successfully in the field of sounds (events) recognition. However, the extension of SVMs to real-world signal processing applications is still an ongoing research topic. Our work consists of illustrating the potential of SVMs on recognizing impulsive audio signals belonging to a complex realworld datas...

A framework for constructing robust oneclass classification models is proposed in the paper. It is based on Walley’s imprecise extensions of contaminated models which produce a set of probability distributions of data points instead of a single empirical distribution. The minimax and minimin strategies are used to choose an optimal probability distribution from the set and to construct optimal ...

Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking only for the labels of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the m...

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally co...