Maximum Likelihood Estimation in the Presence of Errors in Variables: A Modified Approach

Naidu, V. Balakrishnama; Kumar, P. Surya Kumar Dr. P. Surya; Sankar, A. Siva

doi:10.15373/2249555x/june2013/44

Cited by 2 publications

(1 citation statement)

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“…The linear transformations trained in maximum likelihood sense on adaptation data for hidden Markov model based speech recognition is researched in [10], [11]. The authors in [12] discuss and compare the optimal linear estimation and modified MLE, where they show the better results for modified MLE from the point of bias and means square error in comparison with optimal linear model. The maximum likelihood method is used in regression analysis of linear transformation model and efficient and consistent estimator is developed for different data case of cohorts [13], [14].…”

Section: A Related Workmentioning

confidence: 99%

Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware

et al. 2019

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Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.

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