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

Polar Charts In Power BI With R

Leila Etaati shows how to build a polar chart in Power BI using an R component: I just add a layer to the above furmula “coord_polar()” this function also has been used for creating pie charts. it gets the “theta” variable, in below example I put theta=y axis, so we have below charts Normally I […]

Read More

Introduction To Bayesian Statistics

Kennie Nybo Pontoppidan has just completed a course on Bayesian statistics: Last month I finished a four-week course on Bayesian statistics. I have always wondered why people deemed it hard, and why I heard that the computations quickly became complicated. The course wasn’t that hard, and it gave a nice introduction to prior/posterior distributions and […]

Read More

Categories

March 2017
MTWTFSS
« Feb Apr »
 12345
6789101112
13141516171819
20212223242526
2728293031