Pattern Recognition and Big Data

Pattern Recognition and Big Data
by 作者: Amita Pal
ISBN-10 书号: 9813144548
ISBN-13 书号: 9789813144545
Publisher Finelybook 出版日期: 2017-02-28
Pages: 876 pages
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.
Contents:
Pattern Recognition: Evolution,Mining and Big Data
Pattern Classification with Gaussian Processes
Active Multitask Learning using Supervised and Shared Latent Topics
Sparse and Low-Rank Models for Visual Domain Adaptation
Pattern Classification using the Principle of Parsimony: Two Examples
Robust Learning of Classifiers in the Presence of Label Noise
Sparse Representation for Time-Series Classification
Fuzzy Sets as a Logic Canvas for Pattern Recognition
Optimizing Neural Network Structures to Match Pattern Recognition Task Complexity
Multi-Criterion Optimization and Decision Making Using Evolutionary Computing
Rough Sets in Pattern Recognition
The Twin SVM Minimizes the Total Risk
Dynamic Kernels based Approaches to Analysis of Varying Length Patterns in Speech and Image Processing Tasks
Fuzzy Rough Granular Neural Networks for Pattern Analysis
Fundamentals of Rough-Fuzzy Clustering and Its Application in Bioinformatics
Keygraphs: Structured Features for Object Detection and Applications
Mining Multimodal Data
Solving Classification Problems on Human Epithelial Type 2 Cells for Anti-Nuclear Antibodies Test: Traditional versus Contemporary Approaches
Representation Learning for Spoken Term Detection
Tongue Pattern Recognition to Detect Diabetes Mellitus and Non-Proliferative Diabetic Retinopathy
Moving Object Detection using Multi-layer Markov Random Field Model
Recent Advances in Remote Sensing Time Series Image Classification
Sensor Selection for E-Nose
Understanding the Usage of Idioms in Twitter Social Network
Sampling Theorems for Twitter: Ideas from Large Deviation Theory
A Machine-mind Architecture and Z*-numbers for Real-world Comprehension