Machine Learning: Theory and Applications

Statistical learning and analysis techniques have become important today, given the growth in the size of heterogeneous data collections and ability to process it even from physically distant locations. This title familiarize beginners and experts with some of the techniques in this field. It is divided in two sections: Theory and Applications.
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.

The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security.