The problem of having an infinite number of expansion coefficients when using this technique is ameliorated by the existence of the representer theorem.
The problem of having an infinite number of expansion coefficients when using this technique is ameliorated by the existence of the representer theorem. Epsilon-insensitive loss is used as an error measure when performing regression analysis with this technique. John Platt's sequential minimal optimization algorithm speeds up the initial quadratic programming optimization required to use this technique. The appropriate selection of a good kernel and good kernel parameters are necessary to get good results when using this technique. The hinge-loss functionis used in ";soft"; versions of this technique in which data is not linearly separable. For a space of n-dimensional points, this technique selects the n minus 1 dimensional hyperplane with the biggest margin between the data on each side. For 10 points, name this supervised machine- learning technique that non-probabilistically classifies data into two categories.
The problem of having an infinite number of expansion coefficients when using this technique is ameliorated by the existence of the representer theorem.
support vector machines [or SVMs or support vector regression or SVR; prompt on classification or word forms]