ISBN-10:
0444538593
ISBN-13:
9780444538598
Pub. Date:
07/03/2013
Publisher:
Elsevier Science
Handbook of Statistics: Machine Learning: Theory and Applications

Handbook of Statistics: Machine Learning: Theory and Applications

by Elsevier Science

Hardcover

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Product Details

ISBN-13: 9780444538598
Publisher: Elsevier Science
Publication date: 07/03/2013
Series: Handbook of Statistics Series , #31
Pages: 552
Product dimensions: 6.20(w) x 9.10(h) x 1.40(d)

About the Author

Professor C. R. Rao, born in India, is one of this century's foremost statisticians, and received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. He is Emeritus Holder of the Eberly Family Chair in Statistics at Penn State and Director of the Center for Multivariate Analysis. He has long been recognized as one of the world's top statisticians, and has been awarded 34 honorary doctorates from universities in 19 countries spanning 6 continents. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.

In 2011 he was recipient of the Royal Statistical Society's Guy Medal in Gold which is awarded triennially to those "who are judged to have merited a signal mark of distinction by reason of their innovative contributions to the theory or application of statistics". It can be awarded both to fellows (members) of the Society and to non-fellows. Since its inception 120 years ago the Gold Medal has been awarded to 34 distinguished statisticians. The first medal was awarded to Charles Booth in 1892. Only two statisticians, H. Cramer (Norwegian) and J. Neyman (Polish), outside Great Britain were awarded the Gold medal and C. R. Rao is the first non-European and non-American to receive the award.

Other awards he has received are the Gold Medal of Calcutta University, Wilks Medal of the American Statistical Association, Wilks Army Medal, Guy Medal in Silver of the Royal Statistical Society (UK), Megnadh Saha Medal and Srinivasa Ramanujan Medal of the Indian National Science Academy, J.C.Bose Gold Medal of Bose Institute and Mahalanobis Centenary Gold Medal of the Indian Science Congress, the Bhatnagar award of the Council of Scientific and Industrial Research, India and the Government of India honored him with the second highest civilian award, Padma Vibhushan, for “outstanding contributions to Science and Engineering / Statistics”, and also instituted a cash award in honor of C R Rao, “to be given once in two years to a young statistician for work done during the preceding 3 years in any field of statistics”.

For his outstanding achievements Rao has been honored with the establishment of an institute named after him, C.R.Rao Advanced Institute for Mathematics, Statistics and Computer Science, in the campus of the University of Hyderabad, India.



Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the Vice President of Research and Economic Development of the University at Buffalo and founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor’s degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK. He has been a Principal or Co-Investigator of sponsored projects funded for about 65 million dollars. Dr. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently the Editor-in-Chief of the IEEE Biometrics Council Compendium. Dr. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.

Table of Contents

1. The Sequential Bootstrap 2. The Cross-Entropy Method for Estimation 3. The Cross-Entropy Method for Optimization 4. Probability Collectives in Optimization 5. Bagging, Boosting, and Random Forests Using R 6. Matching Score Fusion Methods 7. Statistical Methods on Special Manifolds for Image and Video Understanding 8. Dictionary-based Methods for Object Recognition 9. Conditional Random Fields for Scene Labeling 10. Shape Based Image Classification and Retrieval 11. Visual Search: A Large-Scale Perspective 12. Video Activity Recognition by Luminance Differential Trajectory and Aligned Projection Distance 13. Soft Biometrics for Surveillance: An Overview 14. A User Behavior Monitoring and Profiling Scheme for Masquerade Detection 15. Application of Bayesian Graphical Models to Iris Recognition 16. Learning Algorithms for Document Layout Analysis 17. Hidden Markov Models for Off-Line Cursive Handwriting Recognition 18. Machine Learning in Handwritten Arabic Text Recognition 19. Manifold learning for the shape-based recognition of historical Arabic documents 20. Query Suggestion with Large Scale Data

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