3.2 Classification  (Page 5/2)

Svm

SVM involves three phases: a training phase, cross-validation phase, and testing phase. The training phase learns a model by which to classify new test examples. The cross-validation phase is used to choose parameters of the classifier that will result in the highest accuracy of the classifier.[1] The test phase applies the model with the chosen parameters to classify the test examples. The 150 images of our dataset were divided into 60% training examples, 20% cross-validation examples, and 20% test examples.

Training phase

Figure 1. SVM classifier. [2]

Cross-validation phase

Test phase

Multiclass classification methods

In the training phase of 1 vs 1 classification, a binary classifier is trained for each pair of classes. For example, one of the classifiers would classify all Class I examples as positive examples and all Class 2 examples as negative examples. During the test phase, each of these classifiers is applied to the test data. For each training example, the class that gets the greatest number of positive classifications is chosen as the predicted class.

In the training phase of 1 vs all classification, a single classifier is trained for each of the classes. For example, one of the classifiers would classify all Class I examples as positive examples and all other examples as negative examples. During the test phase, the classifiers are applied to the test data. A single positive class is returned for each training example and is chosen as the predicted class for that example.