Finding cohesive clusters for analyzing knowledge communities (2009)
Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar
Documents and authors can be clustered into “knowledge communities ” based on the overlap in the papers they cite. We introduce a new clustering algorithm, Streemer, which finds cohesive...
Analyzing knowledge communities using foreground and (2009)
Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar
background clusters
Dongyu Lin, Emily Pitler, Dean P. Foster, Lyle H. Ungar
In the past decade, there has been an explosion of interest in using l1-regularization in replace of l0regularization for feature selection. We present results showing that while l1-regularization...
Finding cohesive clusters for analyzing knowledge (2009)
Vasileios K, S. Phineas Upham, Lyle H. Ungar
communities
Winner-Take-All EM Clustering (2009)
Vasileios Kandylas, Lyle H. Ungar, Dean P. Foster
The EM algorithm is often used with mixture models to cluster data, but for efficiency reasons it is sometimes desirable to produce hard clusters. Several hard clustering limits of EM are known. For...
Regularized Learning with Networks of Features (2009)
Ted S, Partha Pratim Talukdar, Lyle H. Ungar, John Blitzer
For many supervised learning problems, we possess prior knowledge about which features yield similar information about the target variable. In predicting the topic of a document, we might know that...
Automatic term list generation for entity tagging (2006)
Sandler, Ted, Schein, Andrew I., Ungar, Lyle H.
Motivation: Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed...
Automatic term list generation for entity tagging (2005)
Sandler, Ted, Schein, Andrew I., Ungar, Lyle H.
Motivation: Many entity-taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed...
The CRASSS plug-in for integrating annotation data with hierarchical clustering results (2004)
Buehler, Eugen C., Sachs, Jeffrey R., Shao, Kui, Bagchi, Ansuman, Ungar, Lyle H.
Summary: We describe an algorithm for finding the most statistically significant non-overlapping subtrees of a hierarchical clustering of gene expression data with respect to a set of secondary data...
The CRASSS plug-in for integrating annotation data with hierarchical clustering results (2004)
Buehler, Eugen C., Sachs, Jeffrey R., Shao, Kui, Bagchi, Ansuman, Ungar, Lyle H.
Summary: We describe an algorithm for finding the most statistically significant non-overlapping subtrees of a hierarchical clustering of gene expression data with respect to a set of secondary data...
The CRASSS plug-in for integrating annotation data with hierarchical clustering results (2004)
Buehler, Eugen C., Sachs, Jeffrey R., Shao, Kui, Bagchi, Ansuman, Ungar, Lyle H.
Summary: We describe an algorithm for finding the most statistically significant non-overlapping subtrees of a hierarchical clustering of gene expression data with respect to a set of secondary data...
Chloroplast transit peptide prediction: a peek inside the black box (2001)
Schein, Andrew I., Kissinger, Jessica C., Ungar, Lyle H.
Previous work in predicting protein localization to the chloroplast organelle in plants led to the development of an artificial neural network-based approach capable of remarkable accuracy in its...