Lossy Compression, Classification, and Regression

1999 ◽  
pp. 49-71
Author(s):  
Robert M. Gray
Keyword(s):  
Lossy Compression ◽  
Classification And Regression

Lossy Compression of Haptic Data by Using DCT

2010 ◽  
Vol 130 (8) ◽  
pp. 945-952 ◽  
Author(s):  
Hiroyuki Tanaka ◽  
Kouhei Ohnishi
Keyword(s):  
Lossy Compression

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images

AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals Gaussian state entanglement witnessing through lossy compression

2021 ◽  
Vol 103 (3) ◽  
Author(s):  
Waldemar Kłobus ◽  
Paweł Cieśliński ◽  
Lukas Knips ◽  
Paweł Kurzyński ◽  
Wiesław Laskowski
Keyword(s):  
Lossy Compression ◽  
Gaussian State
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