PATTERN RECOGNITION AND BIG DATA

Title:

Physical Description:

xvi, 856 pages : illustrations (some color) ; 24 cm.

ISBN:

9789813144545

Abstract:

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 of 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

Subject Term:

Big data

Data mining

Pattern recognition systems

Added Author:

Pal, Amita,

Pal, Sankar K.,

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010343454	QA76.9.B45 P38 2017	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	33000000002387	QA76.9.B45 P38 2017	Open Access Book	Book	Searching... Unknown

On Order

Summary

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.

A. Pal and S. K. PalV. Stathopoulos and M. GirolamiA. Acharya and R. J. Mooney and J. GhoshR. Chellappa and V. M. PatelJ. BasakP. S. Sastry and N. ManwaniS. Bahrampour and N. M. Nasrabadi and A. RayW. Pedryez and N. J. PizziB. G. Gherman and K. Sirlantzis and F. DeraviK. DebA. Skowron and H. S. Nguyen and A. JankowskiJayadeva and S. Soman and S. ChandraVeena T. and Dileep A. D. and C. Chandra SekharA. Ganivada and S. S. Ray and S. K. PalP. Maji and S. PaulM. Hashimoto and H. Morimitsu and R. Hirata-Jr and R. M. Cesar-Jr.S. Chaudhury and L. Dey and I. Verma and E. HassanA. Wiliem and B. C. LuvellP. R. Reddy and K. S. R. Murly and B. YegnanarayanaB. ZhangB. N. Subudhi and S. Ghosh and A. GhoshL. Bruzzone and B. Dermir and F. BovoloSunil T. T. and S. Chaudhuri and M. U. SharmaK. Rudra and A. Chakravarthy and N. Ganguly and S. GhoshD. Palguna and V. Joshi and V. Chakravarthy and R. Kothari and L. V. SubramaniamR. Banerjee and S. K. Pal

Preface	p. vii
1 Pattern Recognition: Evolution, Mining and Big Data	p. 1
2 Pattern Classification with Gaussian Processes	p. 37
3 Active Multitask Learning using Supervised and Shared Latent Topics	p. 75
4 Sparse and Low-Rank Models for Visual Domain Adaptation	p. 113
5 Pattern Classification using the Principle of Parsimony: Two Examples	p. 135
6 Robust Learning of Classifiers in the Presence of Label Noise	p. 167
7 Sparse Representation for Time-Series Classification	p. 199
8 Fuzzy Sots as a Logic Canvas for Pattern Recognition	p. 217
9 Optimizing Neural Network Structures to Match Pattern Recognition Task Complexity	p. 255
10 Multi-Criterion Optimization and Decision Making Using Evolutionary Computing	p. 293
11 Rough Sets in Pattern Recognition	p. 323
12 The Twin SVM Minimizes the Total Risk	p. 395
13 Dynamic Kernels based Approaches to Analysis of Varying Length Patterns in Speech and Image Processing Tasks	p. 407
14 Fuzzy Rough Granular Neural Networks for Pattern Analysis	p. 487
15 Fundamentals of Rough-Fuzzy Clustering and Its Application in Bioinformatics	p. 513
16 Keygraphs: Structured Features for Object Detection and Applications	p. 545
17 Mining Multimodal Data	p. 581
18 Solving Classification Problems on Human Epithelial Type 2 Cells for Anti-Nuclear Antibodies Test: Traditional versus Contemporary Approaches	p. 605
19 Representation Learning for Spoken Term Detection	p. 633
20 Tongue Pattern Recognition to Detect Diabetes Mellitus and Non-Proliferative Diabetic Retinopathy	p. 663
21 Moving Object Detection using Multi-layer Markov Random Field Model	p. 687
22 Recent Advances in Remote Sensing Time Series Image Classification	p. 713
23 Sensor Selection for E-Nose	p. 735
24 Understanding the Usage of Idioms in Twitter Social Network	p. 767
25 Sampling Theorems for Twitter: Ideas from Large Deviation Theory	p. 789
26 A Machine-mind Architecture and Z*-numbers for Real-world Comprehension	p. 805
Author Index	p. 843
Subject Index	p. 845
About the Editors	p. 855

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Table of Contents