Jun'ichi Takeuchi

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Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SmartSifter and empirically demonstrates its(More)
We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., while change-point detection is related to event/trend change detection, activity monitoring, etc.(More)
We are concerned with the issues of outlier detection and change point detection from a data stream. In the area of data mining, there have been increased interest in these issues since the former is related to fraud detection, rare event discovery, etc., while the latter is related to event/trend by change detection, activity monitoring, etc. Specifically,(More)
It is known that the Jeffreys prior plays an important role in statistical inference. In this paper, we generalize the Jeffreys prior from the point of view of information geometry and introduce a one-parameter family of prior distributions, which we named the /spl alpha/-parallel priors. The /spl alpha/-parallel prior is defined as the parallel volume(More)
We consider an on-line learning model of rational choice, in which the goal of an agent is to choose its actions so as to maximize the number of successes, while learning about its reacting environment through th$e very sctions. In particular, we consider a model of tennis play, in which the only actions that the player can take are a ‘pass’ and a ‘lob,’(More)
We discuss the properties of Jeffreys mixture for a Markov model. First, we show that a modified Jeffreys mixture asymptotically achieves the minimax coding regret for universal data compression, where we do not put any restriction on data sequences. Moreover, we give an approximation formula for the prediction probability of Jeffreys mixture for a Markov(More)
This paper is concerned with the problem of detecting outliers from unlabeled data. In prior work we have developed SmartSifter, which is an on-line outlier detection algorithm based on unsupervised learning from data. On the basis of SmartSifter this paper yields a new framework for outlier filtering using both supervised and unsupervised learning(More)
We are developing a technique to predict travel time of a vehicle for an objective road section, based on real time traffic data collected through a probe-car system. In the area of Intelligent Transport System (ITS), travel time prediction is an important subject. Probe-car system is an upcoming data collection method, in which a number of vehicles are(More)