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76
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ICPR
2010
IEEE
15 years 2 months ago
Spatial Representation for Efficient Sequence Classification
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently ...
Pavel Kuksa, Vladimir Pavlovic
HIPC
2005
Springer
15 years 4 months ago
Mobile Pipelines: Parallelizing Left-Looking Algorithms Using Navigational Programming
Abstract. Parallelizing a sequential algorithm—i.e., manually or automatically converting it into an equivalent parallel distributed algorithm—is an important problem. Ideally,...
Lei Pan, Ming Kin Lai, Michael B. Dillencourt, Lub...
96
Voted
SIGIR
2008
ACM
14 years 11 months ago
Learning from labeled features using generalized expectation criteria
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Gregory Druck, Gideon S. Mann, Andrew McCallum
PPOPP
2005
ACM
15 years 4 months ago
A sampling-based framework for parallel data mining
The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data minin...
Shengnan Cong, Jiawei Han, Jay Hoeflinger, David A...
HPCN
1995
Springer
15 years 2 months ago
Using optimistic execution techniques as a parallelisation tool for general purpose computing
Abstract. Optimistic execution techniques are widely used in the field of parallel discrete event simulation. In this paper we discuss the use of optimism as a technique for paral...
Adam Back, Stephen Turner