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
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
Bayesian model averaging(BMA) is another wildly used method which is very like a combining model. However, the difference between BMA and combining models is significant.
The mixture of Gaussians had been discussed in the post 'Mixtures of Gaussians'. It can not only be used to introduce 'EM algorithm' but contain a strategy to improve model performance.
Gaussian mixtures had been discussed in 'Mixtures of Gaussians'. And once we have training data and a certain hypothesis, what we should do next is estimating the parameters of the model. Both kinds of parameters from a mixture 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.
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
Logistic sigmoid function(logistic function for short) had been introduced in post 'An Introduction to Probabilistic Generative Models for Linear Classification'.
'Least-square method' in classification can only deal with a small set of tasks. That is because it was built for the regression task. However, we want a method to solve linear classification especially.