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» Data Mining: Machine Learning, Statistics, and Databases
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SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
13 years 3 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
KDD
2008
ACM
244views Data Mining» more  KDD 2008»
16 years 1 months ago
Probabilistic latent semantic visualization: topic model for visualizing documents
We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-d...
Tomoharu Iwata, Takeshi Yamada, Naonori Ueda
KDD
2000
ACM
121views Data Mining» more  KDD 2000»
15 years 5 months ago
Mining high-speed data streams
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Pedro Domingos, Geoff Hulten
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 1 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
JMLR
2006
135views more  JMLR 2006»
15 years 1 months ago
Statistical Comparisons of Classifiers over Multiple Data Sets
While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more ...
Janez Demsar