SVM: Support Vector Machines and Multi Class Classification

Most of the Classification examples out in the internet talks about binary classification. Also we must understand the learning also applies widely on multi class classification. Good exampleswould be Risk of High/Medium/Low.

According to Wikipedia in machine learning multiclass or multinomial classification is the problem of classifying instances into more than two classes.

Problem:

We would need to test the hypothesis of a patient being in a risk of Heart Attack.

Data Available:

Various set of attributes with pre-defined label of risk of High/Medium/Low as training set of 500 records.

Before we get into the solution we need to understand little bit of SVM.

History of SVM:

Invented by            : Vladimir N Vapnik

Current Standard Proposed by    : Vapnik and Corinna Cortes

Year                 : 1995

Before we even start to understand what is SVM, we need to understand what is Hyperplane. Hyper plane is a concept in geometry if we remember our good school days. We can recollect it well. It is an n-dimensional space. To understand more with examples on hyper planes look at this link.

Similar to any other machine learning techniques, SVMs take some data to start with that’s already classified (the training set), and tries to predict a set of unclassified data (the testing set).

Good URL to know more:

http://www.reddit.com/r/MachineLearning/comments/15zrpp/please_explain_support_vector_machines_svm_like_i/?utm_source=twitterfeed&utm_medium=twitter (I like the most).

http://www.yaksis.com/posts/why-use-svm.html

http://www.cis.upenn.edu/~crammer/code-index.html

We will explore on SVM with a simple example on multi-class classification in my next post.

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