A new concept of a quantum-like mixture model is introduced. It describes the distribution with assumption that point generated by each Gaussian at same time. The improves classification accuracy in machine learning indicating uncertain points should not be assigned to any class. increases on iris data set from 96.67 99.24%.

We propose an extension of the mixture of factor (or independent component) analyzers model to include strongly super-gaussian mixture source densities. This allows greater economy in representation of densities with (multiple) peaked modes or heavy tails than using several Gaussians to represent these features. We derive an EM algorithm to find the maximum likelihood estimate of the model, and...

One of the keystones of the canceled BTeV experiment (proposed at Fermilab’s Tevatron) was its sophisticated threelevel trigger. The trigger was designed to reject 99.9% of lightquark background events and retain a large number of B decays. The BTeV Pixel Detector provided a 3-dimensional, high resolution tracking system to detect B signatures. The Level 1 pixel detector trigger was proposed as...

1.1 Classification Model Before presenting in more details the Gaussian Mixture Model (GMM) classification process, it is worthwhile to consider what “classification” actually means. According to [3], a “classification model” is made of three main parts : • a transducer : in the case of music this would typically be the A/D conversion chain of the sound. • a feature extractor : it extracts sign...

Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further needs to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algorith...

The Dirichlet process is a prior used in nonparametric Bayesian models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as...

This paper investigates the problem of verifying the pronunciations of phonemes from continuous utterances collected from impaired children speakers engaged in a speech therapy session. A new pronunciation verification (PV) approach based on the subspace Gaussian mixture model (SGMM) is presented. A single SGMM is trained from test utterances collected from impaired and unimpaired speakers. PV ...