pattern recognition data analysis (26 Ergebnisse)

Trade Paperback. Zustand: Near Fine. *Excellent* Square, clean, and bright. No marks! A little wear to the exterior. I ship daily. 117 pp. Appendices. Bibliography.

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Zustand: New. pp. 256 97 Illus.

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Paperback. Zustand: Brand New. 228 pages. 8.66x5.91x0.52 inches. In Stock.

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Hardcover. Zustand: Brand New. 2014 edition. 245 pages. 9.75x6.75x0.75 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Zustand: New. This book presents an influence analysis between items in time-stamped databases. It covers developments in data analysis and pattern recognition in multiple databases and details the application of intelligent systems modeling to multiple database analysis. Series: Intelligent Systems Reference Library. Num Pages: 253 pages, 97 black & white illustrations, biography. BIC Classification: UNF; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 14. Weight in Grams: 397. . 2016. Softcover reprint of the original 1st ed. 2014. Paperback. . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware -Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 256 pp. Englisch.

Buch. Zustand: Neu. Neuware -Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 256 pp. Englisch.

Zustand: New. Data Analysis and Pattern Recognition in Multiple Databases Series: Intelligent Systems Reference Library. Num Pages: 253 pages, black & white illustrations. BIC Classification: UNF; UYQP. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 15. Weight in Grams: 537. . 2013. Hardback. . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Paperback. Zustand: Brand New. reprint edition. 256 pages. 9.25x6.10x0.58 inches. In Stock.

Gebunden. Zustand: New.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -This book covers 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 involves 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 modelling 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, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with theintegration 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 856 pp. Englisch.

Buch. Zustand: Neu. Neuware -This book covers 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 involves 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 modelling 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, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with theintegration 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 856 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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).

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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).

Paperback. Zustand: Brand New. 851 pages. 9.25x6.10x1.70 inches. In Stock.

Zustand: New.