Pattern recognition and big data

✍ Scribed by Pal, Amita(Editor);Pal, Sankar Kumar

• Publisher: World Scientific Publishing Company
• Year: 2017
• Tongue: English
• Leaves: 862
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Ttile......Page 5
Preface......Page 9
Contents......Page 17
Chapter 1: Pattern Recognition: Evolution, Mining and Big Data......Page 21
Chapter 2: Pattern Classication with Gaussian Processes......Page 57
Chapter 3: Active Multitask Learning using Supervised andShared Latent Topics......Page 94
Chapter 4: Sparse and Low-Rank Models for Visual DomainAdaptation......Page 132
Chapter 5: Pattern Classification using the Principle of Parsimony:Two Examples......Page 153
Chapter 6: Robust Learning of Classiers in the Presence of LabelNoise......Page 185
Chapter 7: Sparse Representation for Time-Series Classication......Page 216
Chapter 8: Fuzzy Sets as a Logic Canvas for Pattern Recognition......Page 233
Chapter 9: Optimizing Neural Network Structures to Match Pattern Recognition Task Complexity......Page 270
Chapter 10: Multi-Criterion Optimization and Decision Making Using Evolutionary Computing......Page 308
Chapter 11: Rough Sets in Pattern Recognition......Page 337
Chapter 12: The Twin SVM Minimizes the Total Risk......Page 408
Chapter 13: Dynamic Kernels based Approaches to Analysis of VaryingLength Patterns in Speech and Image Processing Tasks......Page 419
Chapter 14: Fuzzy Rough Granular Neural Networks for PatternAnalysis......Page 498
Chapter 15: Fundamentals of Rough-Fuzzy Clustering and Its Application in Bioinformatics......Page 523
Chapter 16: Keygraphs: Structured Features for Object Detection and Applications......Page 554
Chapter 17: Mining Multimodal Data......Page 590
Chapter 18: Solving Classification Problems on Human Epithelial Type2 Cells for Anti-Nuclear Antibodies Test: Traditional versus Contemporary Approaches......Page 614
Chapter 19: Representation Learning for Spoken Term Detection......Page 642
Chapter 20: Tongue Pattern Recognition to Detect Diabetes Mellitusand Non-Proliferative Diabetic Retinopathy......Page 672
Chapter 21: Moving Object Detection using Multi-layer Markov Random Field Model......Page 696
Chapter 22: Recent Advances in Remote Sensing Time Series Image Classification......Page 721
Chapter 23: Sensor Selection for E-Nose......Page 743
Chapter 24: Understanding the Usage of Idioms in the Twitter Social Network......Page 774
Chapter 25: Sampling Theorems for Twitter: Ideas from Large Deviation Theory......Page 796
Chapter 26: A Machine-mind Architecture and Z∗-numbers for Real-world Comprehension......Page 811
Author Index......Page 849
Subject Index......Page 851
About the Editors......Page 861