Pattern Recognition and Big Data

Description

Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis. Readership: Graduate students, researchers, academics and industry practitioners in pattern recognition Content: Pattern recognition: evolution, mining and big data / A. Pal and S.K. Pal -- Pattern classification with Gaussian processes / V. Stathopoulos and M. Girolami -- Active multitask learning using supervised and shared latent topics / A. Acharya, R.J. Mooney and J. Ghosh -- Sparse and low-rank models for visual domain adaptation / R. Chellappa and V.M. Patel -- Pattern classification using the principle of parsimony: two examples / J. Basak -- Robust learning of classifiers in the presence of label noise / P.S. Sastry and N. Manwani -- Sparse representation for time-series classification / S. Bahrampour, N.M. Nasrabadi and A. Ray -- Fuzzy sets as a logic canvas for pattern recognition / W. Pedryez and N.J. Pizzi -- Optimizing neural network structures to match pattern recognition task complexity / B.G. Gherman, K. Sirlantzis and F. Deravi -- Multi-criterion optimization and decision making using evolutionary computing / K. Deb -- Rough sets in pattern recognition / A. Skowron, H.S. Nguyen and A. Jankowski -- The twin SVM minimizes the total risk / Jayadeva, S. Soman and S. Chandra -- Dynamic kernels based approaches to analysis of varying length patterns in speech and image processing tasks / Veena T., Dileep A.D. and C. Chandra Sekhar -- Fuzzy rough granular neural networks for pattern analysis / A. Ganivada, S.S. Ray and S.K. Pal -- Fundamentals of rough-fuzzy clustering and its application in bioinformatics / P. Maji and S. Paul -- Keygraphs: structured features for object detection and applications / M. Hashimoto, H. Morimitsu, R. Hirata-Jr. and R.M. Cesar-Jr. -- Mining multimodal data / S. Chaudhury, L. Dey, I. Verma and E. Hassan -- Solving classification problems on human epithelial type 2 cells for anti-nuclear antibodies test: traditional versus contemporary approaches / A. Wiliem and B.C. Lovell -- Representation learning for spoken term detection / P.R. Reddy, K.S.R. Murty and B. Yegnanarayana -- Tongue pattern recognition to detect diabetes mellitus and non-proliferative diabetic retinopathy / B. Zhang -- Moving object detection using multi-layer Markov random field model / B.N. Subudhi, S. Ghosh and A. Ghosh -- Recent advances in remote sensing time series image classification / L. Bruzzone, B. Demir and F. Bovolo -- Sensor selection for E-Nose / Sunil T.T., S. Chaudhuri and M.U. Sharma -- Understanding the usage of idioms in Twitter social network / K. Rudra, A. Chakraborty, N. Ganguly and S. Ghosh -- Sampling theorems for Twitter: ideas from large deviation theory / D. Palguna, V. Joshi, V. Chakravarthy, R. Kothari and L.V. Subramaniam -- A machine-mind architecture and Z*-numbers for real-world comprehension / R. Banerjee and S.K. Pal.