Search for tag: "svm"

4 Media

03:32 SVM Summary and Comparison SVM Summary and Comparison	SVM Summary and Comparison SVM Summary and Comparison iaml support vector machines regression vectors svm error linear spaces plane trick kernel boundary decision characteristics things points training variables solution thing methods svms From Nigel Goddard 1 likes 1,987 plays 0 Licence Type Creative Commons - Attribution
04:12 SVMs in Practice SVMs in Practice	SVMs in Practice SVMs in Practice iaml support vector machines training vectors domain function basis kernel detection face kernels slide polynomial things methods knowledge thing misclassification questions svm devices categorization From Nigel Goddard 1 likes 2,352 plays 0 Licence Type Creative Commons - Attribution
05:34 Non-Linear SVMs Non-Linear SVMs	Non-Linear SVMs Non-Linear SVMs iaml support vector machines feature space dot vectors dimensions kernel function kind trick spaces algorithms svm point vector training thing classifier transform kernels transforms From Nigel Goddard 1 likes 2,763 plays 0 Licence Type Creative Commons - Attribution
04:01 Optimising the Margin 1 Optimising the Margin 1	Optimising the Margin 1 Optimising the Margin 1 iaml support vector machines margin thing modulus vector lecture reason constraint a2 constraints side point distance problems learning machine svms points solution form svm From Nigel Goddard 2 likes 3,190 plays 0 Licence Type Creative Commons - Attribution

the university of edinburgh

The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336, VAT Registration Number GB 592 9507 00, and is acknowledged by the UK authorities as a “Recognised body” which has been granted degree awarding powers.

Any views expressed within media held on this service are those of the contributors, should not be taken as approved or endorsed by the University, and do not necessarily reflect the views of the University in respect of any particular issue.

Search for tag: "svm"

SVM Summary and Comparison

SVMs in Practice

Non-Linear SVMs

Optimising the Margin 1

the university of edinburgh