Date: 2017-03-17 09:53 pm (UTC)From: [identity profile]

Machine Learning algorithms

Support Vector Machines: The model tries to build a set of hyperplanes in a high dimensional space that tries to separate instances of different classes by getting the largest separation between the nearest instances from different classes. The concept intuitively is simple, but the model can be very complex and powerful. In fact, for some domains it is one of the best Machine Learning algorithms you can use nowadays.
Probabilistic Models: these models usually try to predict the correct response by modeling the problem with a probability distribution. Perhaps the most popular algorithms in this category are Naive Bayes classifiers, that use the Bayes theorem alongside with strong independence assumptions between the features. One of their advantages besides being a simple yet powerful model, is that they return not only the prediction but also the degree of certainty, which can be very useful.
Deep Learning: is a new trend in Machine Learning based on the very known Artificial Neural Network models. Neural networks have a connectionist approach, they try to emulate (in a very simplified way) the way the brain works. Basically they consist of a huge set of interconnected neurons (the basic unit of processing), organized in various layers. Deep learning has, in a few words, developed new structures with deeper layers and improved the learning algorithms to not only try to learn but also to build structures to represent the most important features automatically with higher levels of abstraction.

February 2017

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