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» Approximate data mining in very large relational data
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SDM
2008
SIAM
118views Data Mining» more  SDM 2008»
13 years 8 months ago
Massive-Scale Kernel Discriminant Analysis: Mining for Quasars
We describe a fast algorithm for kernel discriminant analysis, empirically demonstrating asymptotic speed-up over the previous best approach. We achieve this with a new pattern of...
Ryan Riegel, Alexander Gray, Gordon Richards
PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
13 years 5 months ago
Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
VLDB
2004
ACM
227views Database» more  VLDB 2004»
14 years 23 days ago
Approximate NN queries on Streams with Guaranteed Error/performance Bounds
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with...
Nick Koudas, Beng Chin Ooi, Kian-Lee Tan, Rui Zhan...
KDD
2006
ACM
160views Data Mining» more  KDD 2006»
14 years 7 months ago
Coherent closed quasi-clique discovery from large dense graph databases
Frequent coherent subgraphscan provide valuable knowledgeabout the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large...
Zhiping Zeng, Jianyong Wang, Lizhu Zhou, George Ka...
KDD
2010
ACM
222views Data Mining» more  KDD 2010»
13 years 9 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...