Flashcards
Linear and Dual Regression
Cards in this deck (6)
- 1Linear regression modelling guidelines?1) Define model y = w^To(x) + e
2) Our assumption of addative noise and that it has a specific distribution leads to likely
3) Specify prior and get posterior from Gaussian identities - 2In linear regression what is the prediction?We want to see what the updated posterior says about new data
We take the weighted average of all possible weights w and see what the value is at x* and weigh this average by how likely we think the specific w is. - 3Why would we use dual linear regression?Maps a non-linear problem to a different space where it's linear
- 4How to do dual linear regression?Formulate posterior
Find stationary point of posterior
Rewrite w in terms of the data
Kernel regression
Now we can relate new data to old data - 5Kernel regression equation and explanation
- 6Problems with dual linear regression?Lost uncertainty to gain adaptability
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- Machine Learning Flashcards on Linear and Dual Regression, created by Philip Masters on 03/01/2020.
- Created by
- Philip Masters · Jan 2020
- Updated
- Jan 3, 2020
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