Universal filtering and prediction of individual sequences corrupted by noise

A. Somekh Baruch; Neri Merhav

Universal filtering and prediction of individual sequences corrupted by noise

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We address the problem of filtering and prediction of an individual binary sequence based on its noisy past, as an extension to [l]. The performance criterion investigated is the expected fraction of errors. We propose algorithms and compare their performance to that of the best finite state machine (FSM). We improve on previous results [l] by showing that optimum performance can be achieved by Lempel-Ziv-based estimation algorithms.

Original language	American English
Title of host publication	37th Annual Allerton Conference on Communication, Control, and Computing
State	Published - 1999

Bibliographical note

Place of conference:USA

Cite this

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title = "Universal filtering and prediction of individual sequences corrupted by noise",

abstract = "We address the problem of filtering and prediction of an individual binary sequence based on its noisy past, as an extension to [l]. The performance criterion investigated is the expected fraction of errors. We propose algorithms and compare their performance to that of the best finite state machine (FSM). We improve on previous results [l] by showing that optimum performance can be achieved by Lempel-Ziv-based estimation algorithms.",

author = "{Somekh Baruch}, A. and Neri Merhav",

note = "Place of conference:USA",

year = "1999",

language = "American English",

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TY - GEN

T1 - Universal filtering and prediction of individual sequences corrupted by noise

AU - Somekh Baruch, A.

AU - Merhav, Neri

N1 - Place of conference:USA

PY - 1999

Y1 - 1999

N2 - We address the problem of filtering and prediction of an individual binary sequence based on its noisy past, as an extension to [l]. The performance criterion investigated is the expected fraction of errors. We propose algorithms and compare their performance to that of the best finite state machine (FSM). We improve on previous results [l] by showing that optimum performance can be achieved by Lempel-Ziv-based estimation algorithms.

AB - We address the problem of filtering and prediction of an individual binary sequence based on its noisy past, as an extension to [l]. The performance criterion investigated is the expected fraction of errors. We propose algorithms and compare their performance to that of the best finite state machine (FSM). We improve on previous results [l] by showing that optimum performance can be achieved by Lempel-Ziv-based estimation algorithms.

UR - https://scholar.google.co.il/scholar?q=Universal+filtering+and+prediction+of+individual+sequences+corrupted+by+noise&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - 37th Annual Allerton Conference on Communication, Control, and Computing

ER -

Universal filtering and prediction of individual sequences corrupted by noise

Abstract

Bibliographical note

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