Optimization for Perceptron Criterion


### Maximize
set of misclassified samples under w only update until convergence


### Weight Updates
Start with weight 0
**For each training instance**
> - If correct (i.e., y=y*), no change
> - If wrong: adjust the weight vector - adding or subtracting the feature vector (f / y* exactly
> ![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F96bde13f-ce87-46fb-942e-c9964ec7123f%2FUntitled.png?table=block&id=a35c861a-d054-4419-b50c-c672b7a6ae41&cache=v2)
> 

> ![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F3e523c5e-9337-4c60-8453-1396f889d8a9%2FUntitled.png?table=block&id=fdbb578d-a453-4351-a61b-5efbfd34f979&cache=v2)
> 

> x0 bias ican be tan add to f



### Error-Driven Linear Classification
> Binary case - compare features to weight vector
> Learning - figure out weight vector from examples





