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NIPS
2000
15 years 3 months ago
Sparse Representation for Gaussian Process Models
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a...
Lehel Csató, Manfred Opper
ANOR
2002
57views more  ANOR 2002»
15 years 2 months ago
Redefining Event Variables for Efficient Modeling of Continuous-Time Batch Processing
Abstract: We define events so as to reduce the number of events and decision variables needed for modeling batchscheduling problems such as described in [Westenberger and Kallrath ...
Siqun Wang, Monique Guignard
185
Voted
KDD
2010
ACM
435views Data Mining» more  KDD 2010»
15 years 6 months ago
Topic models with power-law using Pitman-Yor process
One of the important approaches for Knowledge discovery and Data mining is to estimate unobserved variables because latent variables can indicate hidden and specific properties o...
Issei Sato, Hiroshi Nakagawa
132
Voted
APCCM
2009
15 years 3 months ago
Conceptional Modeling and Analysis of Spatio-Temporal Processes in Biomolecular Systems
In life science, deeper understanding of biomolecular systems is acquired by computational modeling and analysis. For the modeling of several kinds of reaction networks, e.g. sign...
Andreas Schäfer, Mathias John
INFORMATICALT
2008
162views more  INFORMATICALT 2008»
15 years 2 months ago
Vague Rough Set Techniques for Uncertainty Processing in Relational Database Model
Abstract. The study of databases began with the design of efficient storage and data sharing techniques for large amount of data. This paper concerns the processing of imprecision ...
Karan Singh, Samajh Singh Thakur, Mangi Lal