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. Author manuscript; available in PMC: 2015 Jan 1.
Published in final edited form as: J Exp Psychol Learn Mem Cogn. 2013 Aug 19;40(1):66–85. doi: 10.1037/a0034059

Figure 1.

Figure 1

Example of category classification using support vector machines. The figure depicts a simple 2D example of classification where two categories (A: plus symbols and B: minus symbols) are classified based on two continuous feature dimensions. The goal of the classifier is to determine a decision boundary that maximally separates the most confusing cases from the decision surface (maximum margin classification). The cases that touch the margin are termed support vectors. Note that the dark grey decision boundary results in a greater margin separating the support vectors than the light grey decision boundary.