NDLI: A novel Stochastic Discretized Weak Estimator operating in non-stationary environments

Content Provider	IEEE Xplore Digital Library
Author	Yazidi, A. Oommen, B.J. Granmo, O.
Copyright Year	2012
Description	Author affiliation: School of Computer Science, Carleton University, Ottawa, Canada (Oommen, B.J.) \|\| Dept. of ICT, University of Agder, Grimstad, Norway (Yazidi, A.; Granmo, O.)
Abstract	The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems. A particularly interesting family of distributions are the binomial/multiomial distributions. Existing approaches resort to sliding windows that track changes by discarding old observations. In this paper, we report a novel estimator referred to as the Stochastic Discretized Weak Estimator (SDWE), that is based on the principles of Learning Automata (LA). In brief, the estimator is able to estimate the parameters of a time varying binomial distribution using finite memory. The estimator tracks changes in the distribution by operating on a controlled random walk in a discretized probability space. The steps of the estimator are discretized so that the updates are done in jumps, and thus the convergence speed is increased. The analogous results for binomial distribution have also been extended for the multinomial case. Interestingly, the estimator possesses a low computational complexity that is independent of the number of parameters of the multinomial distribution. The paper briefly reports conclusive experimental results that demonstrate the ability of the SDWE to cope with non-stationary environments with high adaptation rate and accuracy.
Starting Page	364
Ending Page	370
File Size	181071
Page Count	7
File Format	PDF
ISBN	9781467300087
e-ISBN	9781467300094
e-ISBN	9781467307239
DOI	10.1109/ICCNC.2012.6167445
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Weak Estimators Maximum likelihood estimation Non-Stationary Environments Accuracy Learning Automata Automata Vectors Random variables Convergence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A New Family of Weak Estimators for Training in Non-Stationary Distributions (2004)

On utilizing stochastic learning weak estimators for training and classification of patterns with non-stationary distributions.

Ergodic discretized estimator learning automata with high accuracy and high adaptation rate for nonstationary environments

Stochastic automata-based estimators for adaptively compressing files with non-stationary distributions.

Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments

Stochastic Learning-based Weak Estimation of Multinomial Random Variables and Its Applications to Non-stationary Environments (2004)

A maximum-likelihood interpretation of batch means estimators

Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm

A new nonlinear discretized learning automaton with rapid convergence and high accuracy

A novel Stochastic Discretized Weak Estimator operating in non-stationary environments

Similar Documents

A New Family of Weak Estimators for Training in Non-Stationary Distributions (2004)

On utilizing stochastic learning weak estimators for training and classification of patterns with non-stationary distributions.

Ergodic discretized estimator learning automata with high accuracy and high adaptation rate for nonstationary environments

Stochastic automata-based estimators for adaptively compressing files with non-stationary distributions.

Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments

Stochastic Learning-based Weak Estimation of Multinomial Random Variables and Its Applications to Non-stationary Environments (2004)

A maximum-likelihood interpretation of batch means estimators

Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm

A new nonlinear discretized learning automaton with rapid convergence and high accuracy

A novel Stochastic Discretized Weak Estimator operating in non-stationary environments