related topics
{math, number, function}
{theory, work, human}
{ship, engine, design}
{rate, high, increase}
{work, book, publish}
{company, market, business}

Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting is based on the question posed by Kearns[1]: can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well correlated with the true classification.

The affirmative answer to Kearns' question has significant ramifications in machine learning and statistics.


Boosting algorithms


There are many boosting algorithms. The original ones, proposed by Robert Schapire (a recursive majority gate formulation [2]) and Yoav Freund (boost by majority [3]), were not adaptive and could not take full advantage of the weak learners.

Only algorithms that are provable boosting algorithms in the probably approximately correct learning formulation are boosting algorithms. Other algorithms that are similar in spirit to boosting algorithms are sometimes called "leveraging algorithms", although they are also sometimes incorrectly called boosting algorithms.[4]

Examples of boosting algorithms

The main variation between many boosting algorithms is their method of weighting training data points and hypotheses. AdaBoost is very popular and perhaps the most significant historically as it was the first algorithm that could adapt to the weak learners. However, there are many more recent algorithms such as LPBoost, TotalBoost, BrownBoost, MadaBoost, LogitBoost, and others. Many boosting algorithms fit into the AnyBoost framework,[5] which shows that boosting performs gradient descent in function space using a convex cost function. On October 7th, 2010 Phillip Long (at Google) and Rocco A. Servedio (Columbia University) released a paper suggesting that these algorithms are provably flawed in that "convex potential boosters cannot withstand random classification noise," thus making the applicability of such algorithms for real world, noisy data sets questionable.

Full article ▸

related documents
Modus ponens
Tree structure
Recreational mathematics
Lyapunov fractal
Best-first search
LALR parser
Composite number
Complete measure
Partition of unity
Group homomorphism
Linear congruence theorem
Contraction mapping
Nearest neighbour algorithm
Blum Blum Shub
Normal morphism
Zero divisor
RP (complexity)
Multiple inheritance
Axiom of union
Permutation group
Data set
Sophie Germain prime
Zhu Shijie
Baire category theorem
Weak entity
Mutual recursion
Characteristic subgroup
Up to