Support Vector Machine (SVM) in 2 minutes

in machine learning one of the most fundamental tasks is when you have a bunch of objects that you want to classify into two categories or more is this picture of a dog or cat is this stock going up or down svms or support vector machines are some of the simplest and arguably the most elegant methods for classification each object you want to classify is represented as a point in an n-dimensional space and the coordinates of this point are usually called features svms perform the classification test by drawing a hyperplane that is a line in 2d or a plane in 3d in such a way that all points of one category are on one side of the hyperplane and all points of the other category are on the other side and while there could be multiple such hyperplanes svm tries to find the one that best separates the two categories in the sense that it maximizes the distance to points in either category this distance is called the margin and the points that fall exactly on the margin are called the supporting vectors to find this hyperplane in the first place svm requires a training set or a set of points that are already labeled with the correct category this is why svm is said to be a supervised learning algorithm in the background svm solves a convex optimization problem that maximizes this margin and where the constraints say that points of each category should be on the correct side of the hyperplane in practice you don't have to worry about the implementation details of this optimization problem using svm can be as simple as loading a python library preparing your training data feeding it to the fit function and calling predict to assign the correct category to a new object the biggest pros of svms is that they are easy to understand implement use and interpret furthermore they are effective when the size of the training data is small the simplicity of svms can also be a problem in many applications the points cannot be separated by hyperplane a common workaround in this case is 2a augmented data with some nonlinear features that are computed from the existing ones b find the separating hyperplane in this higher dimensional space and c project back to the original space a clever technique known as the kernel track allows us to perform all of these steps in a very efficient manner and now that you know about svms you can use them for face detection spam fill train and text recognition this was svm in about 2 minutes like and subscribe if you liked the video and see you next time

Channel: Visually Explained

Views: 17,001

Keywords:

Id: _YPScrckx28

Length: 2min 19sec (139 seconds)

Published: Thu Sep 09 2021

Reddit Comments

[u/Impressive_Path2037]

I am embarking on a new adventure on youtube where I make short (~2min) videos about various topics in statistics and machine learning. I started with my favorite technique for classification, which is SVM. https://www.youtube.com/watch?v=_YPScrckx28

Obviously, these videos are not intended to cover any given topic in an exhaustive manner, but rather. give a teaser and encourage people to look up more in depth references. It might also serve as a quick reminder for THAT ML concept that you definitely knew how to use a couple of months ago but forgot all about it.

It is definitely a much harder exercise than I had anticipated. It is incredibly difficult to narrow down complicated concept in a small amount of time. Producing animations can be very time consuming as well. I think there is still a lot of room for improvement, so I would appreciate any feedback about this particular format of videos. (Is too fast/too slow? Are the explanations unnecessary complicated or do they not go enough in depth? etc)

I am also curious about your own techniques and tips for making short explanations of complicated topics. And also, if you have ideas for topics to cover next, please let me know.Cheers

[u/Zoidberg_DC]

SVM for face detection? How?

[u/kelkulus]

That animation must have taken a while! Very good video for the time constraints and a good jumping off point to learn more; I subscribed :)