<?xml version="1.0"?>

<!DOCTYPE owl [
	<!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#">
	<!ENTITY rdfs "http://www.w3.org/2000/01/rdf-schema#">
	<!ENTITY xsd "http://www.w3.org/2001/XMLSchema#">
	<!ENTITY owl "http://www.w3.org/2002/07/owl#">
	<!ENTITY cc "http://web.resource.org/cc/#">
	<!ENTITY project "http://ebiquity.umbc.edu/ontology/project.owl#">
	<!ENTITY person "http://ebiquity.umbc.edu/ontology/person.owl#">
	<!ENTITY pub "http://ebiquity.umbc.edu/ontology/publication.owl#">
	<!ENTITY assert "http://ebiquity.umbc.edu/ontology/assertion.owl#">
]>

<!--

This ontology document is licensed under the Creative Commons
Attribution License. To view a copy of this license, visit
http://creativecommons.org/licenses/by/2.0/ or send a letter to
Creative Commons, 559 Nathan Abbott Way, Stanford, California
94305, USA.

-->

<rdf:RDF 
		xmlns:rdf = "&rdf;"
		xmlns:rdfs = "&rdfs;"
		xmlns:xsd = "&xsd;"
		xmlns:owl = "&owl;"
		xmlns:cc = "&cc;"
		xmlns:project = "&project;"
		xmlns:person = "&person;"
		xmlns:pub = "&pub;"
		xmlns:assert = "&assert;">
	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/1016/Continuously-Generalized-Ordinal-Regression-for-Linear-and-Deep-Models">
		<rdfs:label><![CDATA[Continuously Generalized Ordinal Regression for Linear and Deep Models]]></rdfs:label>
		<pub:title><![CDATA[Continuously Generalized Ordinal Regression for Linear and Deep Models]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2022-04-28T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach for modeling ordinal data involves fitting parallel separating hyperplanes that optimize a certain loss function. This assumption offers sample efficient learning via inductive bias, but is often too restrictive in real-world datasets where features may have varying effects across different categories. Allowing class-specific hyperplane slopes creates generalized logistic ordinal regression, increasing the flexibility of the model at a cost to sample efficiency. We explore an extension of the generalized model to the all-thresholds logistic loss and propose a regularization approach that interpolates between these two extremes. Our method, which we term continuously generalized ordinal logistic, significantly outperforms the standard ordinal logistic model over a thorough set of ordinal regression benchmark datasets. We further extend this method to deep learning and show that it achieves competitive or lower prediction error compared to previous models over a range of datasets and modalities. Furthermore, two primary alternative models for deep learning ordinal regression are shown to be special cases of our framework.

<br/><hr><br/>

<p>
<img style="width:100%" src="https://i.imgur.com/XP1wkBK.png"></p>
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:site" content="@ebiquity">
<meta name="twitter:creator" content="@ebiquity" />
<meta property="og:image" content="https://i.imgur.com/XP1wkBK.png" >
<meta property="og:title" content="Continuously Generalized Ordinal Regression for Linear and Deep Models" >
<meta name="twitter:image" content="https://i.imgur.com/XP1wkBK.png">
</p>]]></pub:abstract>
		<pub:pages><![CDATA[28-36]]></pub:pages>
		<pub:note><![CDATA[DOI: 10.1137/1.9781611977172.4]]></pub:note>
		<pub:counter>3298</pub:counter>
		<pub:tag><![CDATA[machinelearning]]></pub:tag>
		<pub:tag><![CDATA[ordinal regression]]></pub:tag>
		<pub:tag><![CDATA[ranking]]></pub:tag>
		<pub:booktitle><![CDATA[SIAM International Conference on Data Mining]]></pub:booktitle>
		<pub:publisher><![CDATA[SIAM]]></pub:publisher>
		<pub:author>
			<rdf:List>
				<rdf:first>
					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Francis/Ferraro"><person:name><![CDATA[Francis Ferraro]]></person:name><rdfs:label><![CDATA[Francis Ferraro]]></rdfs:label></person:Person>
				</rdf:first>
				<rdf:rest>					<rdf:List>
						<rdf:first>
							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Edward/Raff"><person:name><![CDATA[Edward Raff]]></person:name><rdfs:label><![CDATA[Edward Raff]]></rdfs:label></person:Person>
						</rdf:first>
						<rdf:rest>							<rdf:List>
								<rdf:first>
									<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Fred/Lu"><person:name><![CDATA[Fred Lu]]></person:name><rdfs:label><![CDATA[Fred Lu]]></rdfs:label></person:Person>
								</rdf:first>
								<rdf:rest rdf:resource="&rdf;nil" />
							</rdf:List>
						</rdf:rest>
					</rdf:List>
				</rdf:rest>
			</rdf:List>
		</pub:author>
		<pub:firstAuthor>
<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Francis/Ferraro"><person:name><![CDATA[Francis Ferraro]]></person:name><rdfs:label><![CDATA[Francis Ferraro]]></rdfs:label></person:Person>
		</pub:firstAuthor>
		<pub:softCopy><pub:SoftCopy>
			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/1137.pdf]]></pub:softCopyURI>
			<pub:softCopySize>563056</pub:softCopySize>
			</pub:SoftCopy>
			</pub:softCopy>
	</pub:InProceedings>

<rdf:Description rdf:about="">
	<cc:License rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
</rdf:Description>

</rdf:RDF>
