Linear model in lecture 18
In lecture 18("Epilogue"), there is a nice map about machine learning (slide 4/23). One class is "Models" that describes the different algorithms used in machine learning. Some of the algorithms under models are "Linear", "SVM", "Neural Networks", "nearest neighbors", etc.
I have a question about the class of models that are "linear". The most basic linear algorithm is the perceptron. But I would say that SVM (Support Vector Machine)could also be defined as a linear algorithm as well as "Linear Discriminant Analysis" and "linear regression" (not well fitted for classif but it is a linear model). That is, under the linear umbrella, there are many different algorithms. Of course, things starts becoming more difuse when we use ztransform (or kernel) in the linrear algorithms. But still there is the question about the criteria and purpose to create a generic linear class. What was the intention to have a "Linear class" and a different class called "SVM" under the models? 
Re: Linear model in lecture 18
You are right. With the Z transform, many models fall under the linear umbrella. As a matter of terminology, the "straight" linear models such as perceptrons as linear regression are the ones often referred to as linear models.

All times are GMT 7. The time now is 06:33 PM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2020, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.