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 proposed handbook is divided in two sections: Theory and Applications. very relevant to current research challenges faced in various fieldsself-contained reference to machine learning emphasis on applications-oriented techniques.
Contents
pt. I. Theoretical aspects -- pt. II. Object recognition -- pt. III. Biometric systems -- pt. IV. Document analysis.
