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» Properties and computations of matrix pseudospectra
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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 7 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
AAAI
2004
14 years 11 months ago
Spatial Aggregation for Qualitative Assessment of Scientific Computations
Qualitative assessment of scientific computations is an emerging application area that applies a data-driven approach to characterize, at a high level, phenomena including conditi...
Chris Bailey-Kellogg, Naren Ramakrishnan
ACCV
2006
Springer
15 years 1 months ago
Multiple Similarities Based Kernel Subspace Learning for Image Classification
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
BMCBI
2007
157views more  BMCBI 2007»
14 years 10 months ago
Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many i...
Feng Luo, Yunfeng Yang, Jianxin Zhong, Haichun Gao...
CDC
2010
IEEE
120views Control Systems» more  CDC 2010»
14 years 5 months ago
Statistical properties of the error covariance in a Kalman filter with random measurement losses
In this paper we study statistical properties of the error covariance matrix of a Kalman filter, when it is subject to random measurement losses. We introduce a sequence of tighter...
Eduardo Rohr, Damián Marelli, Minyue Fu