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NIPS
2008
14 years 11 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
14 years 12 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 4 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...
ECAI
2008
Springer
14 years 12 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»
14 years 10 months 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