General Equation
Generally the equation for a linear model will have two inputs(but can have any number of inputs) and be written as:
x1*w1 + x2*w2 +b
The goal of the ANN is to find the minimal errored equation which, when given a new input can predict accurate outputs compared to outputs that have been classified or calculated by previous inputs.
Two well known models are:
Linear Regression Model– derives a linear relation between the dependent variable and independent(input) variable.
Linear Classification Model-approximates and maps the probabilty that the (input) independent variable is a class of the dependent variable.
How Does the Linear model succeed in minimizing the error?
The Linear model algorithm starts a compilation of iterations/epochs with a random line equation and gradually descends the error to its minimum value.
This error descendence is achieved by using pre-labeled data points(in case of linear supervised model) or in-time-labels data points(in case of linear unsupervised model) and checking each iteration’s/epoch’s error for each data point according to them.
Each data point’s error is calculated and when the minimum error in all data points converges to a minimum, we stop the iteration and are left the linear equation.
We can then determine which weights have the most effect on the model.
Comments are closed, but trackbacks and pingbacks are open.