[Combining Models] Boosting and AdaBoost
[Combining Models] Boosting and AdaBoost

The committee has an equal weight for every prediction from all models, and it gives little improvement than a single model. Then boosting was built for this problem. Boosting is a technique for combining multiple 'base' classifiers to produce a form of the committee that

[Combining Models] Committees
[Combining Models] Committees

The committee is a native inspiration for how to combine several models(or we can say how to combine the outputs of several models). For example, we can combine all the models by

[Mixture Models] Mixtures of Gaussians
[Mixture Models] Mixtures of Gaussians

We have introduced a mixture distribution in the post 'An Introduction to Mixture Models'. And the example in that post was just two components Gaussian Mixture. However, in this post, we would like to talk about Gaussian mixtures formally. And it severs to motivate the expectation-maximization(EM) algorithm.

[Mixture Models] An Introduction to Mixture Models
[Mixture Models] An Introduction to Mixture Models

We have discussed many machine learning algorithms, including linear regression, linear classification, neural network models and e.t.c, till now. However, most of them are supervised learning, which means a teacher is leading the models to bias to a certain task