Machine Intelligence Signal Processing (53 results)

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Taschenbuch. Condition: Neu. Neuware -This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning ¿ instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics ¿ two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vesselclassification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis ¿ a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning - instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics - two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vesselclassification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis - a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing.

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Paperback. Condition: new. Paperback. Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Taschenbuch. Condition: Neu. Neuware -This book features selected high-quality research papers presented at the International Conference on Machine Intelligence and Signal Processing (MISP 2019), held at the Indian Institute of Technology, Allahabad, India, on September 7¿10, 2019. The book covers the latest advances in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. The topics covered include support vector machines (SVM) and variants like least-squares SVM (LS-SVM) and twin SVM (TWSVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. Further, it discusses the real-time challenges involved in processing big data and adapting the algorithms dynamically to improve the computational efficiency. Lastly, it describes recent developments in processing signals, for instance, signals generated from IoT devices, smart systems, speech, and videos and addresses biomedical signal processing: electrocardiogram (ECG) and electroencephalogram (EEG).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 484 pp. Englisch.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book features selected high-quality research papers presented at the International Conference on Machine Intelligence and Signal Processing (MISP 2019), held at the Indian Institute of Technology, Allahabad, India, on September 7-10, 2019. The book covers the latest advances in the fields of machine learning,big data analytics, signal processing, computational learning theory, and their real-timeapplications. The topics covered include support vector machines (SVM) andvariants like least-squares SVM (LS-SVM) and twin SVM (TWSVM), extreme learning machine(ELM), artificial neural network (ANN), and other areas in machine learning. Further, it discusses thereal-time challenges involved in processing big data and adapting the algorithmsdynamically to improve the computational efficiency. Lastly, it describes recent developmentsin processing signals, for instance, signals generated from IoT devices, smart systems, speech,and videos and addresses biomedical signal processing: electrocardiogram (ECG) andelectroencephalogram (EEG).

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Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

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Paperback. Condition: Brand New. 484 pages. 9.25x6.10x1.18 inches. In Stock.

Taschenbuch. Condition: Neu. Neuware -This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, 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 signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 892 pp. Englisch.

Buch. Condition: Neu. Neuware -This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, 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 signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 892 pp. Englisch.

Condition: New.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing,and their applications in real world. The topics covered in machine learning involve 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 modeling 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, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration 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).

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing,and their applications in real world. The topics covered in machine learning involve 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 modeling 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, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration 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).