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NIPS
2008
15 years 1 months ago
Learning Taxonomies by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Matthew B. Blaschko, Arthur Gretton
DAGM
2008
Springer
15 years 2 months ago
Boosting for Model-Based Data Clustering
In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn t...
Amir Saffari, Horst Bischof
ICASSP
2008
IEEE
15 years 6 months ago
Unsupervised validity measures for vocalization clustering
This paper describes unsupervised speech/speaker cluster validity measures based on a dissimilarity metric, for the purpose of estimating the number of clusters in a speech data s...
Kuntoro Adi, Kristine E. Sonstrom, Peter M. Scheif...
77
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ECAI
2008
Springer
15 years 2 months ago
Multilingual Evidence Improves Clustering-based Taxonomy Extraction
We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clusteri...
Hans Hjelm, Paul Buitelaar
PR
2008
116views more  PR 2008»
15 years 9 days ago
Robust path-based spectral clustering
Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. ...
Hong Chang, Dit-Yan Yeung