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Mathematical Problems in Data Science: Theoretical and Practical Methods
15 Angebote vergleichen
Bester Preis: € 112,79 (vom 02.06.2019)Mathematical Problems in Data Science
ISBN: 9783319797397 bzw. 3319797395, vermutlich in Englisch, Springer Shop, Taschenbuch, neu.
This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful. Soft cover.
Mathematical Problems in Data Science
ISBN: 9783319251271 bzw. 3319251279, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful. eBook.
Mathematical Problems in Data Science: Theoretical and Practical Methods (2015)
ISBN: 9783319251271 bzw. 3319251279, in Englisch, 213 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data recovery, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful., Kindle Edition, Ausgabe: 1st ed. 2015, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2015-12-15, Freigegeben: 2015-12-15, Studio: Springer.
Mathematical Problems in Data Science
ISBN: 9783319797397 bzw. 3319797395, vermutlich in Englisch, neu.
This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix, machine learning method on massive data sets, image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark.This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data recovery, geometric search, and computing models.Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.
Mathematical Problems in Data Science
ISBN: 9783319251271 bzw. 3319251279, vermutlich in Englisch, Springer-Verlag GmbH, Taschenbuch, neu.
Mathematical Problems in Data Science als eBook von Li M. Chen, Zhixun Su, Bo Jiang, Li M. Chen, Zhixun Su (2015)
ISBN: 9783319251271 bzw. 3319251279, in Deutsch, Springer International Publishing, neu.
Mathematical Problems in Data Science ab 75.99 EURO Theoretical and Practical Methods. 1st ed. 2015.
Mathematical Problems in Data Scie (2015)
ISBN: 9783319797397 bzw. 3319797395, vermutlich in Englisch, gebundenes Buch, neu, Nachdruck.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Mathematical Problems in Data Science: Theoretical and Practical Methods
ISBN: 9783319251257 bzw. 3319251252, in Deutsch, Springer International Publishing, Springer International Publishing, Springer International Publishing, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Mathematical Problems in Data Science - Theoretical and Practical Methods
ISBN: 9783319251257 bzw. 3319251252, in Deutsch, Springer-Verlag Gmbh, gebundenes Buch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Mathematical Problems in Data Science
ISBN: 9783319251257 bzw. 3319251252, in Deutsch, Springer Shop, gebundenes Buch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen