A Lightweight Machine Learning Assisted Power Optimization for Minimum Error in NOMA-CRS over Nakagami-m channels

AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

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A work load prediction strategy for power optimization on cloud based data centre using deep machine learning

Evolutionary Intelligence ◽

10.1007/s12065-019-00289-4 ◽

2019 ◽

Cited By ~ 1

Author(s):

P. S. Latha Kalyampudi ◽

P. Venkata Krishna ◽

Sathish Kuppani ◽

V. Saritha

Keyword(s):

Machine Learning ◽

Power Optimization ◽

Work Load ◽

Load Prediction ◽

Data Centre ◽

Prediction Strategy

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Early Prediction of LBW Cases via Minimum Error Rate Classifier: A Statistical Machine Learning Approach

2017 IEEE International Conference on Smart Computing (SMARTCOMP) ◽

10.1109/smartcomp.2017.7947002 ◽

2017 ◽

Cited By ~ 4

Author(s):

Anisha R. Yarlapati ◽

Sudeepa Roy Dey ◽

Snehanshu Saha

Keyword(s):

Machine Learning ◽

Error Rate ◽

Learning Approach ◽

Early Prediction ◽

Statistical Machine Learning ◽

Minimum Error ◽

Machine Learning Approach

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An Extension of the Gamma Test Statistics to Binary Variables and Some Applications

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421510101 ◽

2021 ◽

Vol 35 (10) ◽

pp. 2151010

Author(s):

Alessandro Maria Selvitella ◽

Julio J. Valdés

Keyword(s):

Machine Learning ◽

Regression Model ◽

Point Of View ◽

Continuous Case ◽

Test Statistics ◽

Gamma Test ◽

Binary Variables ◽

Minimum Error ◽

Binary Case

In this paper, we discuss the problem of estimating the minimum error reachable by a regression model given a dataset, prior to learning. More specifically, we extend the Gamma Test estimates of the variance of the noise from the continuous case to the binary case. We give some heuristics for further possible extensions of the theory in the continuous case with the [Formula: see text]-norm and conclude with some applications and simulations. From the point of view of machine learning, the result is relevant because it gives conditions under which there is no need to learn the model in order to predict the best possible performance.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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