The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Support Vector Machines and Applications
Abstract
Support Vector Machines is one of the powerful Machine learning algorithms used for numerous applications. Support Vector Machines generate decision boundary between two classes which is characterized by special subset of the training data called as Support Vectors. The advantage of support vector machine over perceptron is that it generates a unique decision boundary with maximum margin. Kernalized version makes it very faster to learn as the data transformation is implicit. Object recognition using multiclass SVM is discussed in the chapter. The experiment uses histogram of visual words and multiclass SVM for image classification.
Related Content
|
Man Tianxing, Vasiliy Yurievich Osipov, Ildar Raisovich Baimuratov, Natalia Alexandrovna Zhukova, Alexander Ivanovich Vodyaho, Sergey Vyacheslavovich Lebedev.
© 2020.
27 pages.
|
|
Alexey Kashevnik, Nikolay Teslya.
© 2020.
23 pages.
|
|
Sergey Vyacheslavovich Lebedev, Michail Panteleyev.
© 2020.
26 pages.
|
|
Valentin Olenev, Yuriy Sheynin, Irina Lavrovskaya, Ilya Korobkov, Lev Kurbanov, Nadezhda Chumakova, Nikolay Sinyov.
© 2020.
42 pages.
|
|
Konstantin Nedovodeev, Yuriy Sheynin, Alexey Syschikov, Boris Sedov, Vera Ivanova, Sergey Pakharev.
© 2020.
34 pages.
|
|
Andrey Kuzmin, Maxim Safronov, Oleg Bodin, Victor Baranov.
© 2020.
23 pages.
|
|
Alexander Yu. Meigal, Dmitry G. Korzun, Alex P. Moschevikin, Sergey Reginya, Liudmila I. Gerasimova-Meigal.
© 2020.
26 pages.
|
|
|