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KDD
2010
ACM
274views Data Mining» more  KDD 2010»
13 years 11 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 11 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
13 years 9 months ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar
PR
2006
164views more  PR 2006»
13 years 7 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung
ICCV
1998
IEEE
13 years 12 months ago
Condensing Image Databases when Retrieval is Based on Non-Metric Distances
One of the key problems in appearance-based vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performa...
David W. Jacobs, Daphna Weinshall, Yoram Gdalyahu