This paper reports on the development of a novel island model for evolutionary algorithms, which is intrinsically parallel and intended to better utilise resources and outlier sol...
An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism. Outlier detection has many applic...
— Outliers refer to “minority” data that are different from most other data. They usually disturb data mining process. But, sometimes they provide valuable information. Thus,...
Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score ...
Georgy L. Gimel'farb, Patrice Delmas, John Morris,...
Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outlie...
Dragoljub Pokrajac, Aleksandar Lazarevic, Longin J...
— When an adaptive software component is employed to select the best-performing implementation for a communication operation at runtime, the correctness of the decision taken str...
Katharina Benkert, Edgar Gabriel, Michael M. Resch
Current approaches to feature detection and matching in images strive to increase the repeatability of the detector and minimize the degree of outliers in the matching. In this pa...
Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector ...
Ji Zhang, Qigang Gao, Hai H. Wang, Qing Liu, Kai X...
Outlyingness is a subjective concept relying on the isolation level of a (set of) record(s). Clustering-based outlier detection is a field that aims to cluster data and to detect...