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» Large Scale Learning of Active Shape Models
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ESANN
2001
13 years 9 months ago
Transfer functions: hidden possibilities for better neural networks
Abstract. Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide...
Wlodzislaw Duch, Norbert Jankowski
BSN
2006
IEEE
131views Sensor Networks» more  BSN 2006»
14 years 1 months ago
Elaborating Sensor Data using Temporal and Spatial Commonsense Reasoning
Ubiquitous computing has established a vision of computation where computers are so deeply integrated into our lives that they become both invisible and everywhere. In order to ha...
Bo Morgan, Push Singh
ISCA
2011
IEEE
269views Hardware» more  ISCA 2011»
12 years 11 months ago
Power management of online data-intensive services
Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These workloads ...
David Meisner, Christopher M. Sadler, Luiz Andr&ea...
ICIC
2005
Springer
14 years 1 months ago
Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number...
Jianru Xue, Nanning Zheng, Xiaopin Zhong
TAP
2009
Springer
178views Hardware» more  TAP 2009»
14 years 2 months ago
Dynamic Symbolic Execution for Testing Distributed Objects
Abstract. This paper extends dynamic symbolic execution to distributed and concurrent systems. Dynamic symbolic execution can be used in software testing to systematically identify...
Andreas Griesmayer, Bernhard K. Aichernig, Einar B...