Gaussian mixture model classification
Classification Of Ground Moving Object Using Coefficient Of Integrated Bispectrum For Doppler Radar
... target classification using Doppler signature in ground moving object depends on better classification ...target classification using Gaussian Mixture Model(GMM) [2], A hidden ...
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Predicting Precipitation Events Using Gaussian Mixture Model
... on classification performance as compared with the first experi- ment, which is shown in Table ...to model the relationship between fea- tures and ...
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On Semi Supervised Learning of Gaussian Mixture Models for Phonetic Classification
... of Gaussian mixture models using an uni- fied objective function taking both labeled and unlabeled data into ...in classification ac- curacy over the supervised ...
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Performance Comparison of EMD based Noise Classification for different SNR using GMM and k-NN Classifiers
... noise classification using Empirical Mode Decomposition ...for classification and proposes an optimized best suitable feature set for classification of different noisy environments with variation in ...
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EMOTION DETECTION IN SPEECH USING GAUSSIAN MIXTURE MODEL
... by Gaussian mixture models ...emotion classification. Also Gaussian modeling is among the best methods to distinguish emotional classes by the following phonetic parameters: pitch, pitch ...
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Fuzzy Weighted Gaussian Mixture Model for Feature Reduction
... the classification problem and seven feature selection techniques were used for evaluating imbalanced data ...component mixture and discriminated the features based on the relevance in the feature space ...
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Study of Algorithms for Separation of Singing Voice from Music
... used Gaussian mixture model (GMM) as a classifier for the classification of the voice and unvoiced ...signal. Gaussian mixture model (GMM) is a mixture of several ...
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A SPEAKER RECOGNITION SYSTEM USING GAUSSIAN MIXTURE MODEL
... The authors S. Paulose and A. Thomas [10] introduces an automatic speaker recognition system identifies a person from the information contained in the speech signal. These systems are the most user-friendly means of ...
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Online Novelty Detection System: One-Class Classification of Systemic Operation
... different model acceptance threshold scalars (λ) and different number of mixture components ...and mixture components with respect to the number of novel events ...detected. Gaussian ...
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Research on Initialization on EM Algorithm Based on Gaussian Mixture Model
... DOI: 10.4236/jamp.2018.61002 12 Journal of Applied Mathematics and Physics algorithm has an obvious shortcoming: it is very sensitive to the initial value. Therefore, in order to get the parameter estimation of the ...
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Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
... When the semi-supervised algorithm finishes, the active algorithm analyzes the instances that were not ranked and searches among them for the one that provides the largest information gain for the model. In order ...
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Application of -means and Gaussian mixture model for classification of seismic activities in Istanbul
... Results of two cluster analyses, which are based on unsu- pervised technique, used metric rather than a target classifi- cation. Thus, we compare their responses with pre-defined targets that were selected by authors ...
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Segmentation of multi temporal images using gaussian mixture model (GMM)
... [1] Proposed a methodology to characterize urban patterns with very high-resolution images using texture analysis based on local variance, co-occurrence matrices, and wavelets. But this methodology is very sensitive to ...
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Change Detection of AL-Hammar Marsh Using Classification Method Based On Gaussian Mixture Model
... supervised classification was used in this study . Supervised classification relies on the prior knowledge of the location and identity of land cover types that are in the ...supervised ...
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Enhanced load profiling for residential network customers
... Access Model (LARA) – high levels of socioeconomic and geographical disaggregation were noted in the ...Area Classification) to segment UK households into 7 groups with different socio- demographic ...
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Cross Lingual Mixture Model for Sentiment Classification
... ture model (CLMM) to tackle the problem of cross- lingual sentiment ...proposed model can learn previously unseen senti- ment words from large unlabeled data, which are not covered by the limited vocabulary ...
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Document Classification Using a Finite Mixture Model
... Experimental results indicate that our method of using the finite mixture model outperforms the method based on hard clustering of words.. Experimental results also indicate that in some[r] ...
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A Survey on Different Classifier in Speech Recognition Techniques.
... The proposed Emotion Recognition System is as shown in Figure 4 .The speech signal is coded with 16 bits PCM and sampled at 22.05khz.The speech samples are segmented into frames.The fundamental frequency of Speech ...
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Bayesian Portfolio Selection in a Markov Switching Gaussian Mixture Model
... The departure from normality can also be seen from the Bayesian residual test. We first fit the MSGM model with one regime and one state, which is effectively a model of multivariate normal returns. We ...
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Binomial Gaussian mixture filter
... a Gaussian mixture filter, such that components have smaller covariances and cause smaller linearization errors when nonlinear measurements are used for the state ...other Gaussian mixture ...
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