Lifelong learning addresses the challenge of acquiring new knowledge and tackling tasks in a continually evolving environment. Although this thread research has recently received increased interest, most lifelong machine approaches proposed thus far focus on object recognition or classification tasks. In contrast, for anomaly detection are still unexplored. This paper presents method loosely ba...

The most common analysis for fMRI images is activation detection, in which the purpose to find locations brain that respond specific functions, such as visual processing or motor functions by providing related stimuli tasks experiment. On other hand, it also important detect instance triggered. One of powerful techniques can analyze abnormal behavior any data change point (CP) analysis. We sugg...

In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence high-dimensional or non-Euclidean observations. We study non-parametric framework that utilizes nearest neighbor information among the observations changes an online setting. It can be applied data arbitrary dimension as long similarity measure on sample space defined. conside...

Abstract We propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate lack enough data near time series’ tails. Through simulation study, we analyse its behaviour presence early/middle/late mean, and compare performance some frequently used recently ...