by River Kanies
C is the "cost" or score of the choice
Wa is weight for variable A
Wb is weight for variable B
So, choosing from a small number of discrete options is simple...
But what if we have a continuous range of options?
(in multiple dimensions)
Here the "optimal" choice for x is approximately 200
where C(x) is minimum
Cost functions give us a method to choose the seemingly optimal choice in complex scenarios.
But assumptions must always be made. Models are never perfect.
It's a bit more complicated than before...
Specifically: Discrete Event Simulation (DES)
Simulate
Cost
FACTOR
(this represents scheduling time offset. Optimize this value to determine best scheduling times)
Simple Cost Function Plot
Linear regression and error minimization
(often, error = cost)
There are 2 independent variables in a simple line
above: theta values
left: b and a
Uses partial derivatives
Global vs. Local optimum
Guess and check
Good heuristics
Simulated annealing
So essentially...
Any decision can be made with cost function optimization
Even super complex ones...
Input --> Weighted Nodes --> Output
Each output is a "class"
Hypothesis - Example
Adjust weights
(similar to gradient descent)
2 layer cost function
X
Y
Z
Ex:
Question --> Redirect link
Natural Language Classifier: