Classification as NonSymbolic Learning CPSC 386 Artificial Intelligence
Classification as Non-Symbolic Learning CPSC 386 Artificial Intelligence Ellen Walker Hiram College
Symbolic vs. Non-Symbolic learning • If you “open the system up” after it has learned, can the knowledge be easily expressed? • Symbolic uses accessible internal representations • Non-symbolic uses inaccessible internal representations
Classification • Given a set of examples (x, y) where x is input (a vector of values), y is classification • Learn a function y=f(x) that – Returns correct results for all (x, y) pairs in the training set of examples – Generalizes well -- returns correct results for x values not in the training set
Discrete vs. Continuous Features • Color – A set of named values? – Numeric Red, Green, Blue codes? • Size – Large vs. small? – Numeric volume?
Graphing Two Features Height Weight
Linear Separation • The equation of a line is a ‘rule’ that separates classes. • If Ax + By > C, then “above the line” (if equal, then “on the line” and if < then “below the line”) • We can implement these “lines” as rules to create a decision tree as before
What Remains? • Where do I draw the lines? – • Can I always find appropriate lines? – • Yes, and no – again we will see What if there are more variables? – • We’ll see one way when we look at perceptrons Draw planes in 3 D, hyperplanes in 4 D etc. Where do the features come from? – A very good question – we’ll look at one way to get them automatically, soon
- Slides: 7