Overview Biological Neural Networks Cell Topology : Input, Output and Hidden Layers Functional Description Error Functions In the previous blog(lectures), we considered models for regression and classification that comprised linear combinations of fixed basis functions. We saw that such models have useful analytical and computational properties but that their practical applicability was limited by the curse of dimensionality. In order to apply such models to large- scale problems, it is necessary to adapt the basis functions to the data. Support vector machines (SVMs), address this by first defining basis functions that are centred on the training data points and then selecting a subset of these during training. One advantage of SVMs is that, although the training involves nonlinear optimization, the objective function is convex, and so the solution of the optimization problem is relatively straightforward. The number of basis functions in the resulting models is generally muc...
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