An introduction to kernel-based learning algorithms
Identifieur interne : 001C17 ( Main/Exploration ); précédent : 001C16; suivant : 001C18An introduction to kernel-based learning algorithms
Auteurs : K. R. Muller [Allemagne] ; S. Mika ; G. Ratsch ; K. Tsuda ; B. ScholkopfSource :
- IEEE Transactions on Neural Networks [ 1045-9227 ] ; 2001.
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- Pascal (Inist)
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Abstract
This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and kernel principal component analysis (PCA), as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis (VC) theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by finally discussing applications such as optical character recognition (OCR) and DNA analysis.
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Scholkopf</name></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">01-0201283</idno><date when="2001">2001</date><idno type="stanalyst">PASCAL 01-0201283 EI</idno><idno type="RBID">Pascal:01-0201283</idno><idno type="wicri:Area/PascalFrancis/Corpus">000728</idno><idno type="wicri:Area/PascalFrancis/Curation">000065</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000686</idno><idno type="wicri:doubleKey">1045-9227:2001:Muller K:an:introduction:to</idno><idno type="wicri:Area/Main/Merge">001D13</idno><idno type="wicri:Area/Main/Curation">001C17</idno><idno type="wicri:Area/Main/Exploration">001C17</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">An introduction to kernel-based learning algorithms</title><author><name sortKey="Muller, K R" sort="Muller, K R" uniqKey="Muller K" first="K. R." last="Muller">K. R. Muller</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>GMD FIRST</s1><s2>12489 Berlin</s2><s3>DEU</s3><sZ>1 aut.</sZ></inist:fA14><country>Allemagne</country><placeName><region type="land" nuts="3">Berlin</region><settlement type="city">Berlin</settlement></placeName></affiliation></author><author><name sortKey="Mika, S" sort="Mika, S" uniqKey="Mika S" first="S." last="Mika">S. Mika</name></author><author><name sortKey="Ratsch, G" sort="Ratsch, G" uniqKey="Ratsch G" first="G." last="Ratsch">G. Ratsch</name></author><author><name sortKey="Tsuda, K" sort="Tsuda, K" uniqKey="Tsuda K" first="K." last="Tsuda">K. Tsuda</name></author><author><name sortKey="Scholkopf, B" sort="Scholkopf, B" uniqKey="Scholkopf B" first="B." last="Scholkopf">B. Scholkopf</name></author></analytic><series><title level="j" type="main">IEEE Transactions on Neural Networks</title><title level="j" type="abbreviated">IEEE Trans Neural Networks</title><idno type="ISSN">1045-9227</idno><imprint><date when="2001">2001</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE Transactions on Neural Networks</title><title level="j" type="abbreviated">IEEE Trans Neural Networks</title><idno type="ISSN">1045-9227</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>DNA</term><term>Gaussian noise (electronic)</term><term>Kernel Fisher discriminant analysis</term><term>Lagrange multipliers</term><term>Learning algorithms</term><term>Mathematical programming</term><term>Optical character recognition</term><term>Principal component analysis</term><term>Probability distributions</term><term>Regression analysis</term><term>Support vector machines (SVM)</term><term>Theory</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Théorie</term><term>Analyse composante principale</term><term>Reconnaissance optique caractère</term><term>Programmation mathématique</term><term>Analyse régression</term><term>Loi probabilité</term><term>Bruit gaussien(électronique)</term><term>Multiplicateur Lagrange</term><term>DNA</term><term>Algorithme apprentissage</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and kernel principal component analysis (PCA), as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis (VC) theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by finally discussing applications such as optical character recognition (OCR) and DNA analysis.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Berlin</li></region><settlement><li>Berlin</li></settlement></list><tree><noCountry><name sortKey="Mika, S" sort="Mika, S" uniqKey="Mika S" first="S." last="Mika">S. Mika</name><name sortKey="Ratsch, G" sort="Ratsch, G" uniqKey="Ratsch G" first="G." last="Ratsch">G. Ratsch</name><name sortKey="Scholkopf, B" sort="Scholkopf, B" uniqKey="Scholkopf B" first="B." last="Scholkopf">B. Scholkopf</name><name sortKey="Tsuda, K" sort="Tsuda, K" uniqKey="Tsuda K" first="K." last="Tsuda">K. Tsuda</name></noCountry><country name="Allemagne"><region name="Berlin"><name sortKey="Muller, K R" sort="Muller, K R" uniqKey="Muller K" first="K. R." last="Muller">K. R. Muller</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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