Pattern recognition data analysis (28 Ergebnisse)

Trade Paperback. Zustand: Near Fine. Zustand des Schutzumschlags: no jacket (as issued). *Excellent* Square, clean, and bright. No marks! A little wear to the exterior. I ship daily. 117 pp. Appendices. Bibliography.

Paperback. Zustand: Brand New. 2015 edition. 100 pages. 9.30x6.20x0.23 inches. In Stock.

Zustand: New. pp. 280.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Learning Structured Data for Human Action Analysis | A unified approach for visual pattern recognition | Tuan Hue Thi | Taschenbuch | 228 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846548332 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

Zustand: New. In English.

hardcover. Zustand: Gut. 253 Seiten; 9783319034096.3 Gewicht in Gramm: 500.

Zustand: New. In English.

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.…

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.…

Taschenbuch. Zustand: Neu. Data Analysis and Pattern Recognition in Multiple Databases | Animesh Adhikari (u. a.) | Taschenbuch | Intelligent Systems Reference Library | xv | Englisch | 2016 | Springer | EAN 9783319377278 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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

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

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, and may have sticker on cover, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

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

Zustand: New. In English.

Zustand: gut. 2013. Data Analysis and Pattern Recognition in Multiple Databases (Intelligent Systems Reference Library, 61, Band 61) In deutscher Sprache. pages.

Gebunden. Zustand: New.

Zustand: New.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Pattern Recognition and Data Analysis with Applications | Deepak Gupta (u. a.) | Taschenbuch | Lecture Notes in Electrical Engineering | xvi | Englisch | 2023 | Springer | EAN 9789811915222 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. In English.

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).…

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).…

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

Zustand: New.