O'Reilly logo

R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Document retrieval and Support Vector Machine

Support Vector Machine (SVM) is a classification algorithm applicable to both linear and nonlinear data classification. It is based on an assumption: if two classes of data cannot be divided by a hyper-plane, then after mapping the source dataset to sufficient higher dimension spaces, the optimal separating hyper-plane must exist.

Here are two concepts that need to be clearly defined:

  • Linearly separable: This means that a dataset can be divided into the target classes with a linear equation with the input of a training tuple.
  • Nonlinearly separable: This means that none of the linear equations exist in the space with the same dimension as that of the training tuple.

The linear hyper-plane can be represented ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required