Linear Support Vector Machines

Ananda Das explains how linear Support Vector Machines work in classifying spam messages:

Linear SVM assumes that the two classes are linearly separable that is a hyper-plane can separate out the two classes and the data points from the two classes do not get mixed up. Of course this is not an ideal assumption and how we will discuss it later how linear SVM works out the case of non-linear separability. But for a reader with some experience here I pose a question which is like this Linear SVM creates a discriminant function but so does LDA. Yet, both are different classifiers. Why ? (Hint: LDA is based on Bayes Theorem while Linear SVM is based on the concept of margin. In case of LDA, one has to make an assumption on the distribution of the data per class. For a newbie, please ignore the question. We will discuss this point in details in some other post.)

This is a pretty math-heavy post, so get your coffee first. h/t R-Bloggers.

Related Posts

Combining Plots In R With cowplot

Abdul Majed Raja shows how to use the cowplot library in R to merge together independent plots into a single image: The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce […]

Read More

Classifying Texts With Naive Bayes

I continue my series on Naive Bayes with another hand-calculation post: Step two is, on the surface, pretty tough: how do we figure out if a set of words is a business phrase or a baseball phrase? We could try to think up a set of features. For example, how long is the phrase? How many unique […]

Read More


March 2017
« Feb Apr »