Ensemble Learning: Difference between revisions
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==Boosting== | ==Boosting== | ||
Reference [https://cs.nyu.edu/~mohri/mlbook/ Foundations of Machine Learning Chapter 6]<br> | |||
Idea: Build a strong learner from a set of weak learners. | |||
===Adaboost=== | |||
Learn a linear combination of our weak learners. | |||
<pre> | |||
Given a sample of size m | |||
for i=1:m | |||
d_i=1/m | |||
for t=1:T | |||
h_t <- classifier | |||
alpha_t <- (1/2)log((1-eps_t)/eps_t) | |||
z_t <- e[eps_t(1-eps_t)]^(1/2) | |||
for i=1:m | |||
D_{t+1} <- (D_t(i)exp(-alpha_t*y_i*h_t(x_i))/z_t | |||
g <- sum alpha_t h_t | |||
</pre> | |||
==Bagging== | ==Bagging== | ||
Revision as of 15:37, 9 December 2019
Boosting
Reference Foundations of Machine Learning Chapter 6
Idea: Build a strong learner from a set of weak learners.
Adaboost
Learn a linear combination of our weak learners.
Given a sample of size m
for i=1:m
d_i=1/m
for t=1:T
h_t <- classifier
alpha_t <- (1/2)log((1-eps_t)/eps_t)
z_t <- e[eps_t(1-eps_t)]^(1/2)
for i=1:m
D_{t+1} <- (D_t(i)exp(-alpha_t*y_i*h_t(x_i))/z_t
g <- sum alpha_t h_t