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ICDM
2005
IEEE
135views Data Mining» more  ICDM 2005»
14 years 1 months ago
Bit Reduction Support Vector Machine
Abstract— Support vector machines are very accurate classifiers and have been widely used in many applications. However, the training and to a lesser extent prediction time of s...
Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, And...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
ICDE
2008
IEEE
137views Database» more  ICDE 2008»
14 years 9 months ago
Stop Chasing Trends: Discovering High Order Models in Evolving Data
Abstract-- Many applications are driven by evolving data -patterns in web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction...
Shixi Chen, Haixun Wang, Shuigeng Zhou, Philip S. ...
ISORC
2005
IEEE
14 years 1 months ago
Self-Tuning Planned Actions Time to Make Real-Time SOAP Real
This paper proposes a new method for programming and controlling distributed tasks. Applications declare behavior patterns that are used to automatically predict and reserve resou...
Johannes Helander, Stefan B. Sigurdsson
IJBIDM
2010
111views more  IJBIDM 2010»
13 years 6 months ago
Context-aware taxi demand hotspots prediction
: In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contex...
Han-Wen Chang, Yu-chin Tai, Jane Yung-jen Hsu