Abstract
In this chapter, we split the space by a hyperplane and assign a different class to each halfspace. Such rules offer tremendous advantages-they are easy to interpret as each decision is based upon the sign of where x = (JC (1),..., x (d)) and the a i ’s are weights. The weight vector determines the relative importance of the components. The decision is also easily implemented—in a standard software solution, the time of a decision is proportional to d—and the prospect that a small chip can be built to make a virtually instantaneous decision is particularly exciting.
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© 1996 Springer Science+Business Media New York
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Devroye, L., Györfi, L., Lugosi, G. (1996). Linear Discrimination. In: A Probabilistic Theory of Pattern Recognition. Stochastic Modelling and Applied Probability, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0711-5_4
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DOI: https://doi.org/10.1007/978-1-4612-0711-5_4
Publisher Name: Springer, New York, NY
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