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» Evaluating algorithms that learn from data streams
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AUSAI
2007
Springer
16 years 1 months ago
Merging Algorithm to Reduce Dimensionality in Application to Web-Mining
Dimensional reduction may be effective in order to compress data without loss of essential information. Also, it may be useful in order to smooth data and reduce random noise. The...
Vladimir Nikulin, Geoffrey J. McLachlan
NIPS
2008
15 years 8 months ago
One sketch for all: Theory and Application of Conditional Random Sampling
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the o...
Ping Li, Kenneth Ward Church, Trevor Hastie
IPPS
2008
IEEE
16 years 1 months ago
Multi-source video multicast in peer-to-peer networks
We propose a novel framework for multi-source video streaming in peer-to-peer (P2P) networks. Multiple videos are distributed to all requesting peers exploiting full collaboration...
Francisco de Asís López-Fuentes, Eck...
WSDM
2010
ACM
254views Data Mining» more  WSDM 2010»
16 years 4 months ago
Learning Influence Probabilities In Social Networks
Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a ...
Amit Goyal 0002, Francesco Bonchi, Laks V. S. Laks...
ICDE
2008
IEEE
137views Database» more  ICDE 2008»
16 years 8 months ago
XML Prefiltering as a String Matching Problem
We propose a new technique for the efficient search and navigation in XML documents and streams. This technique takes string matching algorithms designed for efficient keyword sear...
Christoph Koch, Stefanie Scherzinger, Michael Schm...