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ICDM
2010
IEEE
198views Data Mining» more  ICDM 2010»
13 years 3 months ago
Hierarchical Ensemble Clustering
Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input)...
Li Zheng, Tao Li, Chris H. Q. Ding
UAI
2003
13 years 6 months ago
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and...
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying...
HYBRID
2000
Springer
13 years 9 months ago
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
Abstract. Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortu...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham
CORR
2010
Springer
110views Education» more  CORR 2010»
13 years 2 months ago
Learning Multi-modal Similarity
In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, including nearest-neighbor retrieval, classification,...
Brian McFee, Gert R. G. Lanckriet
INFORMS
2007
123views more  INFORMS 2007»
13 years 5 months ago
Constructing Ensembles from Data Envelopment Analysis
It has been shown in prior work in management science, statistics and machine learning that using an ensemble of models often results in better performance than using a single ‘...
Zhiqiang Zheng, Balaji Padmanabhan