Tag: over fitting
17. Learning: Boosting
“Wisdom of a weighted crowd of experts” Classifiers Classifiers are tests that produce binary choices about samples. They are considered strong classifiers if their error rate is close to 0, weak classifiers if their error rate is close to 0.5. By using multiple classifiers with different weights, data samples can be sorted or grouped according to different…
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…