【学习】(Python/R)SVM及调参教程

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

1. What is a Support Vector Machine?

2. How does it work?

3. Derivation of SVM Equations

4. Pros and Cons of SVMs

5. 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