March 30, 2017
About the author : Joni holds a PhD in marketing. He is currently working as a postdoctoral researcher at Qatar Computing Research Institute and Turku School of Economics. Contact: joolsa (at) utu.fi
“Please explain Support Vector Machines (SVM) like I am a 5 year old.” #analytics #machinelearning #modeling
Courtesy of @copperking at Reddit: https://www.reddit.com/r/MachineLearning/comments/15zrpp/please_explain_support_vector_machines_svm_like_i
Direct quotation from Reddit:
Boring adults the call balls data, the stick a classifier, the biggest gap trick optimization, call flipping the table kernelling and the piece of paper a hyperplane.”
Well, for a practice-oriented guy the first question obviously is: so what? What can you do with it in practice?
I think it boils down to the nature of classification algorithms. They are quite widely used, e.g. in image or text recognition. So, machine can better learn how to differentiate between an orange and an apple, for example. This of course leads into multiple efficiency advantages, when we are able to replace human classifiers in many jobs.
In conclusion, in my quest to understand machine learning it has become obvious that support vector machine is not the easiest concept to start from. However, since classification is an essential area in machine learning, one cannot avoid it for too long.