Here the first part of this post. Now, we can talk about second part of the text classification example.
Tags » SVM
This post is about classification of text documents via Support Vector Machines. Before text classification, I will try to give a general overview of classification. Machine learning is a subfield of computer science and statistics that study of algorithms which consist of a step in the learning from data. 387 more words
Recall the formula of Support Vector Machines whose solution is global optimum obtained from an energy expression trading off between the generalization of the classifier versus the loss incured when misclassifies some points of a training set , i.e., 676 more words
I’m sorry this took so long. I’m doing something I swore I would never do too, post a chapter without having another one written yet. My muse disappeared after she dumped this one on me so I’m hoping putting a bit of stress on me will make her feel sorry enough for me to make her come back. 4,301 more words
Just last year (Nov 2013), I went on a Future Researchers’ showcase night at the University of Queensland, Brisbane which was more like a info session targeted at potential PhD candidates. 1,245 more words
Fine to skip this section if willing.
As generally known, SVM may be the most powerful and widespread classifier nowadays. However, due to the complicated mathematics inside and some convient libraries like LIBSVM, most of us including me just use it as a blackbox tool without knowing how it works. 1,818 more words