Journal Article10.1016/J.PATCOG.2017.11.022
Bi-weighted ensemble via HMM-based approaches for temporal data clustering
Yun Yang,Jianmin Jiang +1 more
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TL;DR: A novel bi-weighted ensemble is proposed to solve the initialization and automated model selection problems encountered by all HMM-based clustering techniques and their applications and achieves the advantage that the number of clusters can be automatically determined.
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About: This article is published in Pattern Recognition. The article was published on 01 Apr 2018. The article focuses on the topics: Cluster analysis & Hierarchical clustering.
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Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer
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Clustering ensembles: A hedonic game theoretical approach
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TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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