Principal Component Analysis (PCA)
- Support Vector Regression (SVR)
- Neural Network Zoo | Fjodor Van Veen
- [http://www.giocc.com/harnessing-the-grid-ai-with-support-vector-machines.html Harnessing The Grid AI with Support Vector Machines
Posted on Feb 15, 2015 | Gio Carlo Cielo]
Support vector machines (SVM) find optimal solutions for classification problems. Classically they were only capable of categorising linearly separable data; say finding which images are of Garfield and which of Snoopy, with any other outcome not being possible. During training, SVMs can be thought of as plotting all the data (Garfields and Snoopys) on a graph (2D) and figuring out how to draw a line between the data points. This line would separate the data, so that all Snoopys are on one side and the Garfields on the other. This line moves to an optimal line in such a way that the margins between the data points and the line are maximised on both sides. Classifying new data would be done by plotting a point on this graph and simply looking on which side of the line it is (Snoopy side or Garfield side). Using the kernel trick, they can be taught to classify n-dimensional data. This entails plotting points in a 3D plot, allowing it to distinguish between Snoopy, Garfield AND Simon’s cat, or even higher dimensions distinguishing even more cartoon characters. SVMs are not always considered neural networks. Cortes, Corinna, and Vladimir Vapnik. “Support-vector networks.” Machine learning 20.3 (1995): 273-297.
