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About Foundations of Machine Learning: Regression
This document summarizes a lecture on regression in machine learning. It discusses the regression problem, loss functions like mean squared error, and the goal of finding a hypothesis with low generalization error. It then covers topics like empirical error, linear regression, kernel ridge regression, support vector regression, and Lasso. Generalization bounds are presented for finite hypothesis sets and based on Rademacher complexity and pseudo-dimension.
Detailed Information
| Author: | ['zaid ashraf'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 52 |
| Language: | English |
| Format: | |
| Price: | FREE |
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