A Statistical Learning Model of Text Classification for SVMs
There are at least two ways to motivate why a particular learning method is suitable for a particular learning task. Since ultimately one is interested in the performance of the method, one way is through comparative studies. Chapters 6 and 7 present such studies and show that SVMs deliver state-of-the-art classification performance. However, success on benchmarks is a brittle justification for a learning algorithm and gives only limited insight. Therefore, this dissertation takes a different approach. It introduces support vector machines for learning text classifiers from a theoretical perspective.