Introduction
Data classification is a very important task in machine
learning. Support Vector Machines (SVMs) are widely applied in the field
of pattern classifications and nonlinear regressions. The original form
of the SVM algorithm was introduced by Vladimir N. Vapnik and Alexey
Ya. Chervonenkis in 1963. Since then, SVMs have been transformed
tremendously to be used successfully in many real-world problems such as
text (and hypertext) categorization, image
classification, bioinformatics (Protein classification, Cancer
classification), handwritten character recognition, etc.
Table of Contents
What is a Support Vector Machine?
How does it work?
Derivation of SVM Equations
Pros and Cons of SVMs
Python and R implementation
What is a Support Vector Machine(SVM)?
A Support Vector Machine is a supervised machine learning algorithm
which can be used for both classification and regression problems. It
follows a technique called the kernel trick to transform the data and
based on these transformations, it finds an optimal boundary between the
possible outputs.
In simple words, it does some extremely complex data transformations
to figure out how to separate the data based on the labels or outputs
defined.We will be looking only at the SVM classification algorithm in
this article.
链接：
http://blog.hackerearth.com/simple-tutorial-svm-parameter-tuning-python-r
原文链接：
http://weibo.com/1402400261/EwzY1tKxj?type=comment#_rnd1487672939001
