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Unformatted text preview: 6 Linear Models A hyperplane in a space H endowed with a dot product Â· Â· is described by the set { x âˆˆ H  w x + b = 0 } (6.1) where w âˆˆ H and b âˆˆ R . Such a hyperplane naturally divides H into two halfspaces: { x âˆˆ H  w x + b â‰¥ } and { x âˆˆ H  w x + b < } , and hence can be used as the decision boundary of a binary classifier. In this chapter we will study a number of algorithms which employ such linear decision boundaries. Although such models look restrictive at first glance, when combined with kernels (Chapter 5 ) they yield a large class of useful algorithms. All the algorithms we will study in this chapter maximize the margin. Given a set X = { x 1 ... x m } , the margin is the distance of the closest point in X to the hyperplane ( 6.1 ). Elementary geometric arguments (Problem 6.1 ) show that the distance of a point x i to a hyperplane is given by  w x i + b  / w , and hence the margin is simply min i =1 ... m  w x i + b  w . (6.2) Note that the parameterization of the hyperplane ( 6.1 ) is not unique; if we multiply both w and b by the same nonzero constant, then we obtain the same hyperplane. One way to resolve this ambiguity is to set min i =1 ...m  w x i + b  = 1 . In this case, the margin simply becomes 1 / w . We postpone justification of margin maximization for later and jump straight ahead to the description of various algorithms. 6.1 Support Vector Classification Consider a binary classification task, where we are given a training set { ( x 1 y 1 ) ... ( x m y m ) } with x i âˆˆ H and y i âˆˆ {Â± 1 } . Our aim is to find a linear decision boundary parameterized by ( w b ) such that w x i + b â‰¥ 159 160 6 Linear Models x 1 w x 2 y i = âˆ’ 1 y i = +1 { x  w x + b = âˆ’ 1 } { x  w x + b = 1 } { x  w x + b = 0 } w x 1 + b = +1 w x 2 + b = âˆ’ 1 w x 1 âˆ’ x 2 = 2 w w x 1 âˆ’ x 2 = 2 w Fig. 6.1. A linearly separable toy binary classification problem of separating the diamonds from the circles. We normalize ( w b ) to ensure that min i =1 ...m  w x i + b  = 1. In this case, the margin is given by 1 w as the calculation in the inset shows. whenever y i = +1 and w x i + b < 0 whenever y i = âˆ’ 1. Furthermore, as dis cussed above, we fix the scaling of w by requiring min i =1 ...m  w x i + b  = 1. A compact way to write our desiderata is to require y i ( w x i + b ) â‰¥ 1 for all i (also see Figure 6.1 ). The problem of maximizing the margin therefore reduces to max w b 1 w (6.3a) s.t. y i ( w x i + b ) â‰¥ 1 for all i (6.3b) or equivalently min w b 1 2 w 2 (6.4a) s.t. y i ( w x i + b ) â‰¥ 1 for all i. (6.4b) This is a constrained convex optimization problem with a quadratic objec tive function and linear constraints (see Section 3.3 ). In deriving ( 6.4 ) we implicitly assumed that the data is linearly separable, that is, there is a hyperplane which correctly classifies the training data. Such a classifier is called a hard margin classifier . If the data is not linearly separable, then ( 6.4 ) does not have a solution. To deal with this situation we introduce 6.1 Support Vector Classification6....
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 Spring '08
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 Support vector machine, Yi j

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