Reading notes for lecture 13

Text classification is one of the first applications of machine learning, that applies to the general problem of supervised inductive learning: given a set of training documents, classified to one or more predefined categories, learn to automatically classify new documents. Automated text classification has been used in a number of different applications: automatic indexing, content management, filtering, and routing, word sense disambiguation, and Yahoo!-style search space categorization. A plethora of techniques have been developed for text classification,including Nearest-Neighbours, Regression, Neural Networks
Naive Bayes, Decision Trees, and Support Vector Machines. In most cases, the classification algorithms require sufficient training data in order to generalize well on unseen documents.
However, the generalization using labeled examples is an extremely costly and time-consuming activity. The
need for classifiers that can learn from small training samples is imperative. This is an area of active research and several experiments have been conducted to boost conventional classifiers’ performance, by combining supervised learning with semi-supervised or unsupervised, using techniques such as co-training active learning and transductive SVMs .