1998 leopold kindermann 2002 as usual a document d is

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Unformatted text preview: k that the final decision depends only on relatively few terms. A decisive improvement may be achieved by boosting decision trees (Schapire & Singer 1999), i.e. determining a set of complementary decision trees constructed in such a way that the overall error is reduced. Schapire & Singer (2000) use even simpler one step decision trees containing only one rule and get impressive results for text classification. 3.1.5 Support Vector Machines and Kernel Methods A Support Vector Machine (SVM) is a supervised classification algorithm that recently has been applied successfully to text classification tasks (Joachims 1998; Dumais et al. 1998; Leopold & Kindermann 2002). As usual a document d is represented by a – possibly weighted – vector (td1 , . . . , tdN ) of the counts of its words. A single SVM can only separate two classes — a positive class L1 (indicated by y = +1) and a negative class L2 (indicated by y = −1). In the space of input vectors a hyperplane may be defined by setting y = 0 in the following linear equation. y = f (td ) = b0 + 34 N ∑ bj tdj j =1 LDV-FORUM A Brief Survey o...
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