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JMLR
2006
131views more  JMLR 2006»
15 years 1 months ago
Incremental Support Vector Learning: Analysis, Implementation and Applications
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM ...
Pavel Laskov, Christian Gehl, Stefan Krüger, ...
106
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DATAMINE
1999
113views more  DATAMINE 1999»
15 years 1 months ago
A Fast Parallel Clustering Algorithm for Large Spatial Databases
The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper,...
Xiaowei Xu, Jochen Jäger, Hans-Peter Kriegel
DMIN
2009
180views Data Mining» more  DMIN 2009»
14 years 11 months ago
APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining
While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are...
Jimmy Secretan, Anna Koufakou, Michael Georgiopoul...
ICCS
2009
Springer
15 years 8 months ago
Experience with Approximations in the Trust-Region Parallel Direct Search Algorithm
Recent years have seen growth in the number of algorithms designed to solve challenging simulation-based nonlinear optimization problems. One such algorithm is the Trust-Region Par...
S. M. Shontz, V. E. Howle, P. D. Hough
ICPP
2000
IEEE
15 years 6 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary