s of forty-nine regular papers from PAKDD 2005 [Ho et al. 2005], which were not used in the framework building process, were collected and analyzed to see if they fit in the categories identified by grounded theory. The abstract of each article was analyzed to identify the primary objective(s) the author(s) are addressing. Take the article “Adjusting Mixture Weights of Gaussian Mixture Model vi...

Fuzzy Gaussian mixture modeling method is proposed in this paper for network anomaly detection. A mixture of Gaussian distributions was used to represent the network data in multi-dimensional feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the KDD Cup data set. Experimental results have shown that the proposed method is more effective...

We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the mixture of experts model and can also be used for modeling general conditional probability densities. We discuss how Gaussian processes -in particular in form of Gaussian process classification, the support vector mac...

Prior to publication, please maintain the enclosed paper in confidence and use it only for purposes of evaluating the merit of the proposed paper, and other activities reasonably related to the review process, and please do not make it available, in whole or in part, to the public. The authors thanks IEEE Transactions in Speech and Audio Processing for their courtesy and professionalism in this...

Motivated by the need to identify new and clinically relevant categories of lung disease, we propose a novel clustering with constraints method using a Dirichlet process mixture of Gaussian processes in a variational Bayesian nonparametric framework. We claim that individuals should be grouped according to biological and/or genetic similarity regardless of their level of disease severity; there...

Article history: Received 3 June 2009 Received in revised form 1 December 2009 Available online 11 December 2009 Communicated by R.C. Guido

In this paper, the performance of Perceptual Linear Prediction (PLP) features has been compared with the performance of Linear Prediction Coefficient (LPC) features for speaker identification. Two classification techniques, Gaussian Mixture Models (GMM) and Vector Quantization (VQ) with Dynamic time wrapping (DTW) are used for classification of speakers based on their speech samples into respec...

Finding optimum distributed detection schemes is a diicult mathematical problem. Cases with dependent non-Gaussian impulsive noise are of particular interest and have not yet been studied. Here a two-sensor known-signal detection problem is considered where additive impulsive noise, which is dependent from sensor to sensor, corrupts the observations. The noise is modeled as a mixture of Gaussia...

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems. However, many real-world data exhibit cluster distributions that cannot be captured by a single Gaussian. Modeling such data sets by DPMG creates several extraneous clusters even when clusters are relatively well-defined. Herein, we present the infinite mixture of infini...

In this paper we propose a new incremental estimation of Gaussian mixture models which can be used for applications of online learning. Our approach allows for adding new samples incrementally as well as removing parts of the mixture by the process of unlearning. Low complexity of the mixtures is maintained through a novel compression algorithm. In contrast to the existing approaches, our appro...