reference/4021242

Predicate	Object
contentType	Journal Article
issn	1099-4300
issueIdentifier	7
pageRange	707-
publicationName	Entropy (Basel, Switzerland)
startingPage	707
bibliographicCitation	Duan J, Tian X, Ma W, Qiu X, Wang P, An L. Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion. Entropy (Basel). 2019 Jul 19;21(7). PMID: 33267421; PMCID: PMC7515222.
creator	http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_4897b2c90fb93002e10248e77feca8bb http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_ac4c17cb8fb4f6231b54657c8a6e05ac http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_dc3660315f9a98e8f6287a45951c7c5e http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_a8f690995390e2724c84034e733d929a http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_f26ac9c5ce1d6262035d5617215611b8 http://rdf.ncbi.nlm.nih.gov/pubchem/author/ORCID_0000-0002-2781-1693
date	2019-07-19^^<http://www.w3.org/2001/XMLSchema#date>
identifier	https://doi.org/10.3390/e21070707 https://pubmed.ncbi.nlm.nih.gov/33267421 https://pubmed.ncbi.nlm.nih.gov/PMC7515222
isPartOf	https://portal.issn.org/resource/ISSN/1099-4300 http://rdf.ncbi.nlm.nih.gov/pubchem/journal/36201
language	English
source	https://www.crossref.org/ https://pubmed.ncbi.nlm.nih.gov/
title	Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

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