Neural-gas for function approximation: a heuristic for minimizing the local estimation error

This paper considers the robust estimation fusion problem for distributed multisensor systems with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty.

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Semi-supervised dimensionality reduction based on local estimation error

International Journal of High Performance Computing and Networking ◽

10.1504/ijhpcn.2019.10021107 ◽

2019 ◽

Vol 14 (1) ◽

pp. 69

Author(s):

Jie Li ◽

Yongming Cai ◽

Zhiwen Yu ◽

Guihua Wen ◽

Xianfa Cai ◽

...

Keyword(s):

Dimensionality Reduction ◽

Estimation Error ◽

Local Estimation

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A "recruiting neural-gas" for function approximation

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium ◽

10.1109/ijcnn.2000.861286 ◽

2000 ◽

Cited By ~ 1

Author(s):

M. Aupetit ◽

P. Couturier ◽

P. Massotte

Keyword(s):

Function Approximation ◽

Neural Gas

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Adaptive function approximation in reinforcement learning with an interpolating growing neural gas

2012 12th International Conference on Hybrid Intelligent Systems (HIS) ◽

10.1109/his.2012.6421387 ◽

2012 ◽

Author(s):

Michael Baumann ◽

Hans Kleine Buning

Keyword(s):

Reinforcement Learning ◽

Function Approximation ◽

Adaptive Function ◽

Growing Neural Gas ◽

Neural Gas

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Semi-supervised dimensionality reduction based on local estimation error

International Journal of High Performance Computing and Networking ◽

10.1504/ijhpcn.2019.099745 ◽

2019 ◽

Vol 14 (1) ◽

pp. 69 ◽

Cited By ~ 1

Author(s):

Xianfa Cai ◽

Jia Wei ◽

Guihua Wen ◽

Zhiwen Yu ◽

Yongming Cai ◽

...

Keyword(s):

Dimensionality Reduction ◽

Estimation Error ◽

Local Estimation

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Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/331 ◽

2018 ◽

Author(s):

Haifang Li ◽

Yingce Xia ◽

Wensheng Zhang

Keyword(s):

Function Approximation ◽

Estimation Error ◽

Approximation Error ◽

Random Projection ◽

Finite Sample ◽

Computation Efficiency ◽

Feature Spaces ◽

Linear Function Approximation ◽

Projection Techniques

Policy evaluation with linear function approximation is an important problem in reinforcement learning. When facing high-dimensional feature spaces, such a problem becomes extremely hard considering the computation efficiency and quality of approximations. We propose a new algorithm, LSTD(lambda)-RP, which leverages random projection techniques and takes eligibility traces into consideration to tackle the above two challenges. We carry out theoretical analysis of LSTD(lambda)-RP, and provide meaningful upper bounds of the estimation error, approximation error and total generalization error. These results demonstrate that LSTD(lambda)-RP can benefit from random projection and eligibility traces strategies, and LSTD(lambda)-RP can achieve better performances than prior LSTD-RP and LSTD(lambda) algorithms.

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Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

2016 ◽

Vol 63 (6) ◽

pp. 333-342

Author(s):

Roberto Limongi ◽

Angélica M. Silva

Keyword(s):

Response Time ◽

Short Term Memory ◽

Estimation Error ◽

Predictive Coding ◽

Time Estimation ◽

Prediction Errors ◽

Memory Scanning ◽

Short Term ◽

Term Memory ◽

Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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