Feature Clustering using Gaussian Mixture Model
Fuzzy Weighted Gaussian Mixture Model for Feature Reduction
... In feature reduction, a number of techniques are proposed to select the relevant features from the ...є[0,1] using Maximum Weighted Likelihood ...selected using Ordered Weighted Average (OWA) ...
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Speaker Recognition using Gaussian Mixture Model
... mode[sy using text dependent speech, the individual utters either a fixed password or prompted phrase that is programmed into the system and this type of system can improve performance especially with cooperative ...
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Enhancing Clustering Mechanism by Implementation of EM Algorithm for Gaussian Mixture Model
... data clustering in machine learning&computer ...useful model from data & applying algorithms to extraction of no hide information Due to increasing amount of data available online, World Wide Web ...
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Semi-supervised Feature Extraction Method Using Partial Least Squares and Gaussian Mixture Model
... PLS 63,91 83,78 VI. C ONCLUSIONS We introduced a new kernel version of an algorithm for semi-supervised feature extraction. Our algorithm uses weighted separation criterion to find the weights vector, which allows ...
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Predicting Precipitation Events Using Gaussian Mixture Model
... a Gaussian mixture model (GMM) based classifier is described to tell whether precipitation events will happen on a certain day at a certain time from historical meteorological ...classifier ...
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A SPEAKER RECOGNITION SYSTEM USING GAUSSIAN MIXTURE MODEL
... The authors Tomi Kinnunen and Haizhou Li [1] implemented an independent speaker remembrance technology, with an important on text-independent placement. He has done work on speaker recognition actively for nearly ten ...
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EMOTION DETECTION IN SPEECH USING GAUSSIAN MIXTURE MODEL
... machines using speech signal is difficult since machines do not have sufficient integellence to analyze emotion from ...emotions. Feature extraction is the first step for speaker ...(MFCC) feature ...
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Fuzzy Weighted Ordered Weighted Average-Gaussian Mixture Model for Feature Reduction
... optimal feature subset using machine learning techniques and evaluation ...by using the multi criterion decision ...by using the WOWA criteria in fuzzy ...features using the weights in ...
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Fused Mel Feature sets based Text-Independent Speaker Identification using Gaussian Mixture Model
... implemented using any of the following techniques: Hidden Markov Model (HMM) [1], Gaussian Mixture Model (GMM) [2], Vector Quantization (VQ) [3], Neural Networks (NN) [4] and Discrete ...
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GAUSSIAN MIXTURE MODEL (GMM) BASED K MEANS METHOD FOR SPEECH CLUSTERING
... speech clustering, the extraction of speech features is the initial part of the work, and regarding to this we use the HTK [5] tool kit; it extracts the speech data directly into MFCC [7] raw ...speech ...
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Deep unsupervised clustering with Gaussian mixture variational autoencoders
... a mixture of Gaussians as our prior, as it is an intuitive extension of the uni- modal Gaussian ...a mixture of Gaussians, inferring the class of a data point is equivalent to inferring which mode of ...
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A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach
... The post processing stage involves the matching process to obtain the scores for different input (test) images. The scores thus obtained have to be fused by the proposed fusion technique at the match score level, ...
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Speech to Text Converter Using Gaussian Mixture Model(GMM)
... by using Gaussian Mixture ...by using 16-bit Pulse code modulation with a sampling rate of 8KHz and it is stored as a wave file by using sound recorder software in ...by using ...
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Pairwise Fuzzy Ordered Weighted Average Algorithm-Gaussian Mixture Model for Feature Reduction
... Introduction Feature reduction is a challenging problem of finding the significant features in data mining ...in feature selection, extraction and ...addressed using the pairwise feature ...
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Serial and parallel implementations of model based clustering via parsimonious Gaussian mixture models
... Abstract Model-based clustering using a family of Gaussian mixture models, with parsimo- nious factor analysis-like covariance structure, is described and an efficient algorithm for its ...
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Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set
... than Gaussian Mixture Model ...the clustering might not need to be done at every frame, but at a given interval or when changes ...the Gaussian Mixture Model ...knowledge ...
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CiteSeerX — The Infinite Gaussian Mixture Model
... is Gaussian with component parameters j and s j ...are Gaussian and Gamma shaped) in order to generate a Monte Carlo estimate of the probability of “generating a new ...that using a single sample ...
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Classifying Exoplanets with Gaussian Mixture Model
... classification using density and the two-dimensional classi- fication using density and ...the Gaussian-mixture Model (GMM) [ 10 ], which is part of the Scikit-learn package, used for a ...
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High-Dimensional Non-Gaussian Data Clustering using Variational Learning of Mixture Models
... over model parameters analyti- cally thanks to the accurate choice of specific conjugate ...the mixture parameters and the number of ...a model with the correct number of components has been based ...
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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
... particular model cannot be ...avoided using the Bayesian regularisation proposed in Fraley and Raftery ( 2007a ) and implemented in mclust as described in Fraley et ...the model covariances ...
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