AdaBoost: Decision Stumps

Related lectures (29)

Page 3 of 3

Covers weak learners in boosting, AdaBoost algorithm, drawbacks, simple weak learners, boosting variants, and Viola-Jones Haar-Like wavelets.

Explores moment-generating functions, linear combinations, and normality of random variables.

Covers decision forests, training, weak learners, entropy, boosting, 3D pose estimation, and practical applications.

Explores linear systems solutions, vector equations, and equality between vectors.

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores specific solutions and modal base in a generalized conservative oscillator.

Explores the canonical representation of discrete signals as a linear combination of dispersed pulses and other related concepts.

Explains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.