Tag: decision boundaries
16. Learning: Support Vector Machines
Decision boundaries Separating positive and negative example with a straight line that is as far as possible from both positive and negative examples, a median that maximizes the space between positive and negative examples. Constraints are applied to build a support vector (u) and define a constant b that allow to sort positive examples from…
11. Learning: Identification Trees, Disorder
Identification Trees Problems Non-numeric data Not all characteristics matter Some do matter but not all of the time Cost: certain tests may be more expensive than others Occam’s razor The objective is to build the smallest tree possible (to reduce costs and computation) and because the simplest explanation is always the best. Testing data Small…
10. Introduction to Learning, Nearest Neighbors
Learning Regularity: “Bulldozer computing” Nearest neighbors: pattern recognition Neural nets: mimic biology Boosting: theory Constraints: human-like learning One-shot Explanation-based learning Nearest neighbor A detection mechanism generates a vector of features. These features are converted in a vector of values that is compared to a library of possibilities to find the closest match in order to…