Desktop Survival Guide
by Graham Williams


Usage: Classification tasks, regression and other modelling.
Input: Training data consisting of entities expressed as attribute-value pairs, with a class associated with each entity.
Output: An ensemble of models which are to be deployed together with their decisions being combined to give a joint decision.
Complexity: Depends on complexity of the weak learner employed, but generally the weak learner is quite simple (e.g., OneR or Decision Stumps) hence scalability is generally good.
Availability: Freely available in Weka (See Chapter [*]) and in R (See Chapter [*]). Commercial data mining toolkits implementing AdaBoost include TreeNet (See Chapter [*]), Statistica (See Chapter [*]), and Virtual Predict (See Chapter [*]).

Copyright © 2004-2006 [email protected]
Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.