Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia, outliers happen because of different test conditions, human errors, abnormal variations in context, etc. In this paper, we propose a simple yet effective algorithm for outlier detection and robust QoE evaluation named...

Intrusion Detection System (IDS) is a potential part in the area of network security system. An effective intrusion detection system is necessary for providing effective communications in the past world. The major challenging task in this system is the classification of users such as normal user and attacker. For that purpose so many classification algorithms have been proposed in the past to d...

Using a model developed by Relethford (1992), we assess temporal trends (1750-1949) in marital migration in the Aland Islands, Finland, in relation to both geographic distance and population size. The 200-year time period was divided into four 50-year periods. For all time periods both geographic distance and population size are important determinants of migration among 15 Lutheran parishes. Th...

Article history: Received 19 August 2014 Received in revised form 9 September 2014 Accepted 11 September 2014 Available online 21 September 2014 We examine learning-by-exporting effects of manufacturing and services firms in 19 sub-Saharan African countries. Comparing several outlier-robust estimators, our results provide evidence for positive effects in the manufacturing sector when using the ...

This paper addresses a new framework for designing robust neural network classifiers, The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present a modified likelihood function which incorporate the potential risk of outliers in the data. This ...

Outlier detection and cluster number estimation is an important issue for clustering real data. This paper focuses on spectral clustering, a timetested clustering method, and reveals its important properties related to outliers. The highlights of this paper are the following two mathematical observations: first, spectral clustering’s intrinsic property of an outlier cluster formation, and secon...