Results 11 to 20 of about 7,207 (210)
GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 Concept drift is a change in the joint probability distribution of the problem. This term can be subdivided into two types: real drifts that affect the conditional probabilities p(y|x) or virtual drifts that affect the unconditional probability distribution p(x). Most existing work focuses on dealing with real concept drifts.
Gustavo H. F. M. Oliveira +2 more
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Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
Sensors, 2016 In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
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Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models
Information, 2023 Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general ...
Diyar Fadhil, Rodolfo Oliveira
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Meikuang Anquan, 2020 An online detection method for longitudinal tear of mine conveyor belt is proposed, and the method combines infrared image features with improved Gaussian Mixture Model(GMM). An adaptive hybrid median filtering technique is designed.
GUO Jian, QIAO Tiezhu, CHE Jian
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S-TRANSFORM AND GAUSSIAN MIXTURE MODEL FOR ACOUSTIC SCENE CLASSIFICATION
International Journal of Advances in Signal and Image Sciences, 2020 In this study, Acoustic Scene Classification (ASC) system is designed with the help of S-transform and Gaussian Mixture Model (GMM). The S-transform is an extension of continuous wavelet transform that combines the progressive resolution with phase ...
Santosh Kumar Srivastava
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Clustering compositional data using Dirichlet mixture model.
PLoS ONE, 2022 A model-based clustering method for compositional data is explored in this article. Most methods for compositional data analysis require some kind of transformation. The proposed method builds a mixture model using Dirichlet distribution which works with
Samyajoy Pal, Christian Heumann
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Kernel Analysis Based on Dirichlet Processes Mixture Models
Entropy, 2019 Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels.
Jinkai Tian, Peifeng Yan, Da Huang
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EURASIP Journal on Advances in Signal Processing, 2004 We propose a self-splitting Gaussian mixture learning (SGML) algorithm for Gaussian mixture modelling. The SGML algorithm is deterministic and is able to find an appropriate number of components of the Gaussian mixture model (GMM) based on a self ...
Shih-Sian Cheng +2 more
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The learning method of robot teaching sewing motion
Xi'an Gongcheng Daxue xuebao, 2022 In order to realize the robot′s learning of teaching sewing motion, a robot motion learning method based on Gaussian Mixture Model (GMM) -Gaussian Mixture Regression (GMR) was proposed.
WANG Haoyi, WANG Xiaohua, WANG Wenjie
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Adaptive Gaussian Mixture Model for Uncertainty Propagation Using Virtual Sample Generation
Applied Sciences, 2023 Orbit uncertainty propagation plays an important role in the analysis of a space mission. The accuracy and computation expense are two critical essences of uncertainty propagation.
Tianlai Xu, Zhe Zhang, Hongwei Han
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