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» Evaluating algorithms that learn from data streams
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CCGRID
2010
IEEE
15 years 4 months ago
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds
Data-intensive parallel applications on clouds need to deploy large data sets from the cloud's storage facility to all compute nodes as fast as possible. Many multicast algori...
Tatsuhiro Chiba, Mathijs den Burger, Thilo Kielman...
ICRA
1998
IEEE
106views Robotics» more  ICRA 1998»
15 years 11 months ago
Learning a Linear Association of Drilling Profiles in Stapedotomy Surgery
The two-level fuzzy-lattice (2L-FL) learning scheme is introduced for application on an intelligent surgical (mechatronic) drill in the stapedotomy surgical procedure in the ear. ...
Vassilis G. Kaburlasos, Vassilios Petridis, Peter ...
CORR
2006
Springer
99views Education» more  CORR 2006»
15 years 6 months ago
PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation Assumption
Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...
Jon Feldman, Ryan O'Donnell, Rocco A. Servedio
IJCNN
2008
IEEE
16 years 1 months ago
Incremental Common Spatial Pattern algorithm for BCI
— A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI syst...
Qibin Zhao, Liqing Zhang, Andrzej Cichocki, Jie Li
ICDM
2010
IEEE
134views Data Mining» more  ICDM 2010»
15 years 4 months ago
Consequences of Variability in Classifier Performance Estimates
The prevailing approach to evaluating classifiers in the machine learning community involves comparing the performance of several algorithms over a series of usually unrelated data...
Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla