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INFFUS
2008
97views more  INFFUS 2008»
15 years 6 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
IJCNN
2006
IEEE
16 years 2 days ago
Non-Relevance Feedback Document Retrieval based on One Class SVM and SVDD
— This paper reports a new document retrieval method using non-relevant documents. Especially, this paper reports a comparison of retrieval efficiency between One Class Support ...
Takashi Onoda, Hiroshi Murata, Seiji Yamada
IJBRA
2010
133views more  IJBRA 2010»
15 years 3 months ago
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and ...
Mona Soliman Habib, Jugal Kalita
JMLR
2006
156views more  JMLR 2006»
15 years 6 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
MINENET
2006
ACM
16 years 1 hour ago
SVM learning of IP address structure for latency prediction
We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Suppor...
Robert Beverly, Karen R. Sollins, Arthur Berger