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» Generalized clustering, supervised learning, and data assign...
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138
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IDA
2010
Springer
15 years 2 months ago
Multi-dimensional data construction method with its application to learning from small-sample-sets
Insufficient training data is one of the major problems in neural network learning, because it leads to poor learning performance. In order to enhance an intelligent learning proc...
Hsiao-Fan Wang, Chun-Jung Huang
114
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SIGIR
2008
ACM
15 years 3 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
125
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KDD
2003
ACM
210views Data Mining» more  KDD 2003»
16 years 4 months ago
Privacy-preserving k-means clustering over vertically partitioned data
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key ...
Jaideep Vaidya, Chris Clifton
131
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CBMS
2005
IEEE
15 years 5 months ago
Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis
Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before apply...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen
138
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KDD
2004
ACM
158views Data Mining» more  KDD 2004»
16 years 4 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...