Support Vector Machines In R

Deepanshu Bhalla explains what support vector machines are:

The main idea of support vector machine is to find the optimal hyperplane (line in 2D, plane in 3D and hyperplane in more than 3 dimensions) which maximizes the margin between two classes. In this case, two classes are red and blue balls. In layman’s term, it is finding the optimal separating boundary to separate two classes (events and non-events).

Deepanshu then goes on to implement this in R.

Related Posts

Defining TF-IDF

Bruno Stecanella explains the concept behind TF-IDF: TF-IDF was invented for document search and information retrieval. It works by increasing proportionally to the number of times a word appears in a document, but is offset by the number of documents that contain the word. So, words that are common in every document, such as this, what, and if, rank […]

Read More

Defining Tidy Data

John Mount shares thoughts about the concept of tidy data: A question is: is such a data set “tidy”? The paper itself claims the above definitions are “Codd’s 3rd normal form.” So, no the above table is not “tidy” under that paper’s definition. The the winner’s date of birth is a fact about the winner […]

Read More

Categories

January 2017
MTWTFSS
« Dec Feb »
 1
2345678
9101112131415
16171819202122
23242526272829
3031