Feature reduction is one kind of pattern recognition and decision making technique, which can be achieved by using Fuzzy Weighted Gaussian Mixture Model (FWGMM) based on the Gaussian Mixture Model. This model helps to find relevant features by using Fuzzy ordered weighted average, which leads to determine the similarity of the density mixture. The salient feature of this approach is to find the...

The categorization of multidimensional data into clusters is a common task in statistics. Many applications clustering, including the majority tasks ecology, use that inherently spatial and often also temporal. However, spatiotemporal dependence typically ignored when clustering multivariate data. We present finite mixture model for incorporates autocorrelation by appropriate Gaussian processes...

In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective ...

Figure 1.1: Visualization of Tensors of different orders. gained popularity in parameter estimation for a variety of problems. In this lecture, the focus is on how they may be used in estimating the parameters of Gaussian Mixture Models and Hidden Markov Models. In a Gaussian mixture model, there are k unknown n-dimensional multivariate Gaussian distributions. Samples are generated by first pic...