##### [Linear Classification] Logistic Regression

Logistic sigmoid function(logistic function for short) had been introduced in post 'An Introduction to Probabilistic Generative Models for Linear Classification'.

##### [Linear Classification] An Introduction to Probabilistic Generative Models

The generative model used for making decisions contains the inference step and decision step

##### [Linear Classification] Fisher Linear Discriminant(LDA)

'Least-square method' in classification can only deal with a small set of tasks. That is because it was built for the regression task. However, we want a method to solve linear classification especially.

##### [Linear Classification] Least Squares in Classification

Least-squares for linear regression had been talked in 'Simple Linear Regression'. And in this post, we want to find out whether this powerful algorithm can be used in classification.

##### [Linear Classification] Discriminant Functions and Decision Boundary

The discriminant function or discriminant model is on the other side of 'the generative model'. So we, here, have a look at the behave of discriminant function in linear classification

##### [Linear Classification] From Linear Regression to Linear Classification

In the posts 'Introduction to Linear Regression', 'Simple Linear Regression' and 'Polynomial Regression and Features-Extension of Linear Regression', we had discussed the regression task. The goal of regression is to find out a function or hypothesis that given an input $\boldsymbol{x}$, the hypothesis can make a prediction $\hat{y}$ which should be as close to the target $y$ as possible.