Pattern Recognition and Data Analysis with Applications

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processingand their applications in real world. The topics covered in machine learning involves feature extraction, variants ofsupport vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and otherareas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use ofgeometric techniques, scene understanding and modelling from video, 3D object recognition, localization andtracking, medical image analysis and so on. Computational learning theory involves different kinds of learning likeincremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-timechallenges involved while processing big data analytics and stream processing with theintegration of smart datacomputing services and interconnectivity. Additionally, it covers the recent developments to network intelligence foranalyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In thelast, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals,for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signalprocessing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG)and electromyogram (EMG).