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» Clustering by pattern similarity in large data sets
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CVPR
2008
IEEE
16 years 8 months ago
Incremental learning of nonparametric Bayesian mixture models
Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group b...
Ryan Gomes, Max Welling, Pietro Perona
ESEM
2007
ACM
15 years 10 months ago
Using Context Distance Measurement to Analyze Results across Studies
Providing robust decision support for software engineering (SE) requires the collection of data across multiple contexts so that one can begin to elicit the context variables that...
Daniela Cruzes, Victor R. Basili, Forrest Shull, M...
EDM
2008
97views Data Mining» more  EDM 2008»
15 years 8 months ago
Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to it...
Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koe...
ICANN
2011
Springer
14 years 10 months ago
Cross-Species Translation of Multi-way Biomarkers
Abstract. We present a Bayesian translational model for matching patterns in data sets which have neither co-occurring samples nor variables, but only a similar experiment design d...
Tommi Suvitaival, Ilkka Huopaniemi, Matej Oresic, ...
BMCBI
2007
126views more  BMCBI 2007»
15 years 6 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...