Gavin Brown, Ensemble Learning, 2010
http://www.cs.man.ac.uk/~gbrown/research/brown10ensemblelearning.pdf
"Ensemble Learning refers to the procedures employed to train multiple learning machines and combine their outputs, treating them as a "committee" of decision makers. The principle is that the committee decision, with individual predictions combined appropriately, should have better overall accuracy, on average, than any individual committee member. Numerous empirical and theoretical studies have demonstrated that ensemble models very often attain higher accuracy than single models."
"The members of the ensemble might be predicting real-valued numbers, class labels, posterior probabilities, rankings, clusterings, or any other quantity. Therefore, their decisions can be combined by many methods, including averaging, voting, and probabilistic methods. The majority of ensemble learning methods are generic, applicable across broad classes of model types and learning tasks."
Autmatically exploit the strengths and weaknesses of different learning systems
Gavin Brown, Ensemble Learning, 2010
"If we can understand precisely why, when, and how particular ensemble methods can be applied successfully, we will have made progress toward a powerful new tool for machine learning: the ability to automatically exploit the strengths and weaknesses of different learning systems."
Higher accuracy than individual classifier
Amir Amit, Ensemble Classification via Dimensionality Reduction
http://www1.idc.ac.il/toky/msc/Thesis/amit11.pdf
"Ensembles of classifers mimic the human nature to seek advice from several people before making a decision where the underlying assumption is that combining the opinions will produce a decision that is better than each individual opinion."
Can use weak learner models and combine them
"Combines a set of trained weak learner models and data on which these learners were trained. It can predict ensemble response for new data by aggregating predictions from its weak learners. It also stores data used for training and can compute resubstitution predictions. It can resume training if desired."
These are a few examples of AI applications that can use machine learning with ensemble classification:
Machine vision
Character recognition
Face recognition
Speech recognition
Spam detection
Medical diagnosis
Financial fraud detection
Classification algorithms
http://en.wikipedia.org/wiki/Category:Classification_algorithms
Videos:
Caltech: Lecture 01 - The Learning Problem
https://www.youtube.com/watch?v=mbyG85GZ0PI
Introduction to Machine Learning, Purdue Univeristy
https://www.youtube.com/watch?v=tdfZm-_Ru3c&index=1&list=PL2A65507F7D725EFB