Support vector machine, a universal

Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory.
It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition.
Only a part of samples or support vectors (SVs) plays an important role in the final decision function.
But SVs could not be obtained in advance until a quadratic programming is performed.
In this paper, we use K-nearest neighbour method to extract a boundary vector set which may contain SVs.
The number of the boundary set is smaller than the whole training set.
Consequently it reduces the training samples, speeds up the training of support vector machine.

ผลลัพธ์ (แอฟริกา) 1:

ondersteuning vektor masjien, 'n universele metode vir die aanleer van data, winste sy ontwikkeling gebaseer op statistiese leerteorie.
Dit wys baie voordele in die oplossing van nonlinearly klein voorbeeld en 'n hoë dimensionele probleme van patroonherkenning.
Net 'n deel van die monsters of ondersteuning vektore (SVS) speel 'n belangrike rol in die finale besluit funksie.
maar SVS kon nie vooraf verkry word totdat 'n kwadratiese programmering word gedoen
in hierdie vraestel gebruik ons ​​k-naaste metode om 'n grens vektor stel wat mag SVS te onttrek..
Die nommer van die grens stel is kleiner as die hele opleiding stel.
Gevolglik is dit verminder die opleiding monsters, versnel die opleiding van ondersteuning vektor masjien.
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