172.97.220.91: /* Bayesian SVM */ MOS:RELTIME

2025-10-23T10:04:05Z

Bayesian SVM: MOS:RELTIME

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	\| bibcode = 1990JPhA...23L.581O		\| bibcode = 1990JPhA...23L.581O
	\| url = https://dx.doi.org/10.1088/0305-4470/23/11/012		\| url = https://dx.doi.org/10.1088/0305-4470/23/11/012
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	More formally, a support vector machine constructs a [[hyperplane]] or set of hyperplanes in a high or infinite-dimensional space, which can be used for [[Statistical classification\|classification]], [[Regression analysis\|regression]], or other tasks like outliers detection.<ref>{{cite web \|url=http://scikit-learn.org/stable/modules/svm.html \|title=1.4. Support Vector Machines — scikit-learn 0.20.2 documentation \|access-date=2017-11-08 \|url-status=live \|archive-url=https://web.archive.org/web/20171108151644/http://scikit-learn.org/stable/modules/svm.html \|archive-date=2017-11-08 }}</ref> Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class (so-called functional margin), since in general the larger the margin, the lower the [[generalization error]] of the classifier.<ref>{{cite book \|first1=Trevor \|last1=Hastie \|author-link=Trevor Hastie \|first2=Robert \|last2=Tibshirani \|author-link2=Robert Tibshirani \|first3=Jerome \|last3=Friedman \|author-link3=Jerome H. Friedman \|title=The Elements of Statistical Learning : Data Mining, Inference, and Prediction \|location=New York \|publisher=Springer \|edition=Second \|year=2008 \|page=134 \|url=https://web.stanford.edu/~hastie/Papers/ESLII.pdf#page=153 }}</ref> A lower [[generalization error]] means that the implementer is less likely to experience [[overfitting]].		More formally, a support vector machine constructs a [[hyperplane]] or set of hyperplanes in a high or infinite-dimensional space, which can be used for [[Statistical classification\|classification]], [[Regression analysis\|regression]], or other tasks like outliers detection.<ref>{{cite web \|url=http://scikit-learn.org/stable/modules/svm.html \|title=1.4. Support Vector Machines — scikit-learn 0.20.2 documentation \|access-date=2017-11-08 \|url-status=live \|archive-url=https://web.archive.org/web/20171108151644/http://scikit-learn.org/stable/modules/svm.html \|archive-date=2017-11-08 }}</ref> Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class (so-called functional margin), since in general the larger the margin, the lower the [[generalization error]] of the classifier.<ref>{{cite book \|first1=Trevor \|last1=Hastie \|author-link=Trevor Hastie \|first2=Robert \|last2=Tibshirani \|author-link2=Robert Tibshirani \|first3=Jerome \|last3=Friedman \|author-link3=Jerome H. Friedman \|title=The Elements of Statistical Learning : Data Mining, Inference, and Prediction \|location=New York \|publisher=Springer \|edition=Second \|year=2008 \|page=134 \|url=https://web.stanford.edu/~hastie/Papers/ESLII.pdf#page=153 }}</ref> A lower [[generalization error]] means that the implementer is less likely to experience [[overfitting]].

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172.97.220.91: /* Bayesian SVM */ MOS:RELTIME

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