Detection of anomalous network traffic is accomplished using a generalized likelihood ratio test (GLRT) applied to traffic arrival times. The network traffic arrival times are modelled using a Markov modulated Poisson process (MMPP). The GLRT is implemented using an estimate of the MMPP parameter obtained from training data that is not anomalous. MMPP parameter estimation is accomplished using ...

Accurate automated image segmentation in neonates is challenging due to the lower contrast-to-noise ratio compared to adult scans, the partial volume effect and large anatomical variation. In this paper, we present a technique for brain segmentation into different tissues and structures of interest. Atlas priors and subject-specific tissue priors are used to initialize an ExpectationMaximizatio...

Nonnegativity constraints arise frequently in statistical learning and pattern recognition. Multiplicative updates provide natural solutions to optimizations involving these constraints. One well known set of multiplicative updates is given by the ExpectationMaximization algorithm for hidden Markov models, as used in automatic speech recognition. Recently, we have derived similar algorithms for...

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale exper...

In this paper, we improve an unsupervised learning method using the ExpectationMaximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation (WSD) problems. The improved method stops the EM algorithm at the optimum iteration number. To estimate that number, we propose two methods. In experiments, we solved 50 noun WSD pro...

Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler d...

We introduce a clustering method that combines the flexibility of Gaussian mixtures with the scaling properties needed to construct visual vocabularies for image retrieval. It is a variant of expectationmaximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative natur...

The present article aims to point and interval estimation of the parameters of generalised exponential distribution (GED) under progressive interval type-I (PITI) censoring scheme with random removals. The considered censoring scheme is most useful in those cases where continuous examination is not possible. Maximum likelihood, expectationmaximization and Bayesian procedures have been developed...

We study the decision theory of a maximally risk-averse investor — one whose objective, in the face of stochastic uncertainties, is to minimize the probability of ever going broke. With a view to developing the mathematical basics of such a theory, we start with a very simple model and obtain the following results: a characterization of best play by investors; an explanation of why poor and ric...

The Kaplan-Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the ‘constrained’ Kaplan-Meier. Current computational method uses expectationmaximization algorithm to achieve this, but can be slow at many situations. In this note we give a recursive computational algorithm for the ‘constrained’ Kaplan-Meier estimator. The constraint is assumed given i...