29-06-2012, 04:07 PM
Types of Machine Learning Algorithms
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Supervised Learning Approach
Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition,
once again, is a common example of classification learning. More generally, classification
learning is appropriate for any problem where deducing a classification is useful and the
classification is easy to determine. In some cases, it might not even be necessary to give predetermined
classifications to every instance of a problem if the agent can work out the
classifications for itself. This would be an example of unsupervised learning in a
classification context. often leaves the probability for inputs undefined. This model is not
needed as long as the inputs are available, but if some of the input values are missing, it is
not possible to infer anything about the outputs. Unsupervised learning, all the observations
are assumed to be caused by latent variables, that is, the observations is assumed to be at the
end of the causal chain. Examples of supervised learning and unsupervised learning are
shown in the figure 1 below.
Unsupervised learning
Unsupervised learning seems much harder: the goal is to have the computer learn how to
do something that we don't tell it how to do! There are actually two approaches to
unsupervised learning. The first approach is to teach the agent not by giving explicit
categorizations, but by using some sort of reward system to indicate success. Note that this
type of training will generally fit into the decision problem framework because the goal is
not to produce a classification but to make decisions that maximize rewards. This approach
nicely generalizes to the real world,