Figure 3.

The comparison of OC-SVM, TC-SVM and OP-SVM on banana data. The green dots are negative examples and the red crosses are positive examples. The black contours represent the decision boundaries. The differences of algorithms are most clearly visible near positive examples. The OC-SVM ignores the training data labels and its decision boundary includes all the positive examples. The TC-SVM generates a complex decision boundary to separate the positive examples from the negative examples. The decision boundary of the OP-SVM is smoother than the others, and does not include or exclude all the positive examples. OC-SVM, one-class support vector machine classification; OP-SVM, one-plus-class support vector machine; TC-SVM, two-class support vector machine classification.