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KDD
2003
ACM
157views Data Mining» more  KDD 2003»
16 years 7 months ago
Cross-training: learning probabilistic mappings between topics
Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign l...
Sunita Sarawagi, Soumen Chakrabarti, Shantanu Godb...
ICDM
2007
IEEE
122views Data Mining» more  ICDM 2007»
16 years 27 days ago
Zonal Co-location Pattern Discovery with Dynamic Parameters
Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an im...
Mete Celik, James M. Kang, Shashi Shekhar
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 7 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
KDD
2010
ACM
310views Data Mining» more  KDD 2010»
15 years 10 months ago
An integrated machine learning approach to stroke prediction
Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early...
Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Ku...
PKDD
2010
Springer
158views Data Mining» more  PKDD 2010»
15 years 5 months ago
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
Katya Scheinberg, Irina Rish