Classification using Gaussian Mixture Models (GMMs)
Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
... automatic/semi-automatic classification of microalgae based on semi-supervised and active learning algorithms, using Gaussian mixture ...
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A two-stage approach using Gaussian mixture models and higher-order statistics for a classification of normal and pathological voices
... and Λ P indicate the thresholds of the LLR estimated by each GMM for normal and pathological voices. An ex- ample is shown in Figure 3 with false acceptance and false rejection plots versus LLR thresholds. Both lines ...
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On Semi Supervised Learning of Gaussian Mixture Models for Phonetic Classification
... From the previous experiments, we found that the hybrid criterion and purely generative criterion al- most match each other in performance and are able to learn models of the same complexity. This implies that the ...
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Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
... (for models with free dimensions) were done by a 5-fold cross- ...correct classification rate on the learning subset estimated by 5-fold cross-validation (learning CV-CCR), the value of the BIC criterion, ...
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Hybrid generative-discriminative training of Gaussian mixture models
... discriminative models do not capture the input distribution of the data, their use in missing data scenarios is ...train Gaussian mixture models (GMMs) in a hybrid gen- erative-discriminative ...
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Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
... the classification accuracy actually decreases as the number of features increases ...of classification, these problems are related to the curse of dimensionality ...
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Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease
... We employ a clustering method (GMM) to group brain voxels into small regions that exhibit both high similar intensity and geometric affinity. A PET image can be viewed as three dimen- sional (3D) spatial data along with ...
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Supervised and unsupervised classification using mixture models
... MIXTURE MODELS ST´ EPHANE GIRARD AND J´ ER ˆ OME SARACCO ...the classification. The role of Gaussian mixture models is emphasized leading to Linear Discriminant Analysis and ...
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VoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms
... GMM classification and GP for modeling and predicting characteristics of VoIP ...and Gaussian Mixture Model Probabilistic Stochas- tic Histogram ...
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Speaker identification using distributed vector quantization and Gaussian mixture models
... which the first is aimed to improve the accuracy rate of the identification, while the second is aim to reduce the processing time. Several attempts have been made to improve the accuracy rate. Minghui et al. (2006) use ...
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Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis
... Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis Saurabh Prasad, Member, IEEE, Minshan Cui, Wei Li, and James ...with classification driven by Gaussian ...
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Contact-state Modeling of Robotic Assembly Tasks Using Gaussian Mixture Models
... Furthermore, the computational time of building the models was measured for the CFC, EM-GMM, SGB, and GS-FCA to be 0.0014, 26.635, 129.899, and 333.184 sec respectively. It can be noticed that the CFC modeling ...
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Bayesian estimation and classification with incomplete data using mixture models
... tions. Mixture models provide a powerful general semi-parametric method for model- ling densities and have close links to radial basis function neural networks ...to Gaussian mixture ...
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VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS AARON NICHIE
... of using the voice signals for the purpose of identification has found many useful applications in platforms such as access control of information, access to banking services, secured database access system, ...
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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
... avoided using the Bayesian regularisation proposed in Fraley and Raftery ( 2007a ) and implemented in mclust as described in Fraley et ...more models, e.g. by increasing the number of mixture ...
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Gas turbine engine condition monitoring using Gaussian mixture and hidden Markov models
... This can be seen as the ‘elbow’ of the data distribution in Fig- ure 9. There are two other branches of the data distribution which can be attributed to the behaviour of the VSV when it is moving in positive and negate ...
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Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification
... process mixture model (DPMM), a non- parametric approach to learn mixture models that also infers the number of clusters in a data-driven ...object classification [11], scene classi- fication ...
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Maximum likelihood estimation of Gaussian mixture models using stochastic search
... implemented using randomized selection, swapping, addition, and perturbation of the individual parameters of the candidate ...solution using a vector that is created using some combination of random ...
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Dependent Gaussian mixture models for source separation
... Abstract Source separation is a common task in signal processing and is often analogous to factor analysis. In this study, we look at a factor analysis model for source separation of multi-spectral image data where prior ...
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Gaussian Mixture Models for Signal Mapping and Positioning
... use Gaussian Mixtures (GMs) for modeling joint distributions of the position and the RSS ...it models the joint distribution of RSS measurements and the location ...proposed models can model any RSS ...
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