muders thomas (8 Ergebnisse)

Zustand: New. pp. 88.

Taschenbuch. Zustand: Neu. Classification of Medical Data with HTM Cortical Learning Algorithms | Examination of the proposed HTM algorithm with real medical data | Thomas Muders | Taschenbuch | 88 S. | Englisch | 2013 | AV Akademikerverlag | EAN 9783639491920 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Medical data analysis is an important task and the lives of patients often depend on it. However, the analysis itself is often repetitive and time-consuming. Algorithms for this tasks need to have a very high success rate and need to be understandable to work with their parameters. This work will examine a general algorithm for various data types: The Hierarchical Temporal Memory learning algorithm (HTM). It is proposed to be very flexible and to work on any kind of data. The HTM algorithm was implemented and tested with two datasets: Medical images on the one hand and ECG recordings on the other. The proposed algorithm is only presented with a limited data input method and a small input size by its authors. These properties needed to be modified to support a bigger amount of data and also various data types. This is done with an own implementation of the HTM algorithm. The results of this work are analyzed and thoughts and ideas for future development are given at the end. 88 pp. Englisch.

Zustand: New. Print on Demand pp. 88 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Zustand: New. PRINT ON DEMAND pp. 88.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Muders ThomasThomas Muders, M.Sc.: Studied Computer Science at the Leibniz University of Hanover with core subjects Datamining, Databases and Artificial Intelligence.Medical data analysis is an important task and the lives of pat.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Medical data analysis is an important task and the lives of patients often depend on it. However, the analysis itself is often repetitive and time-consuming. Algorithms for this tasks need to have a very high success rate and need to be understandable to work with their parameters. This work will examine a general algorithm for various data types: The Hierarchical Temporal Memory learning algorithm (HTM). It is proposed to be very flexible and to work on any kind of data. The HTM algorithm was implemented and tested with two datasets: Medical images on the one hand and ECG recordings on the other. The proposed algorithm is only presented with a limited data input method and a small input size by its authors. These properties needed to be modified to support a bigger amount of data and also various data types. This is done with an own implementation of the HTM algorithm. The results of this work are analyzed and thoughts and ideas for future development are given at the end.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Medical data analysis is an important task and the lives of patients often depend on it. However, the analysis itself is often repetitive and time-consuming. Algorithms for this tasks need to have a very high success rate and need to be understandable to work with their parameters. This work will examine a general algorithm for various data types: The Hierarchical Temporal Memory learning algorithm (HTM). It is proposed to be very flexible and to work on any kind of data. The HTM algorithm was implemented and tested with two datasets: Medical images on the one hand and ECG recordings on the other. The proposed algorithm is only presented with a limited data input method and a small input size by its authors. These properties needed to be modified to support a bigger amount of data and also various data types. This is done with an own implementation of the HTM algorithm. The results of this work are analyzed and thoughts and ideas for future development are given at the end.