Michael R. Berthold – författare
880 kr
Skickas inom 5-8 vardagar
865 kr
Läs direkt efter köp
Advances in Intelligent Data Analysis XVIII
18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings
454 kr
Skickas inom 10-15 vardagar
585 kr
Skickas inom 5-8 vardagar
786 kr
Läs direkt efter köp
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.
Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.
Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.
This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one''s exploration of thesubject.422 kr
Skickas inom 5-8 vardagar
550 kr
Skickas inom 10-15 vardagar
712 kr
Läs direkt efter köp
1 123 kr
Skickas inom 10-15 vardagar
1 852 kr
Skickas inom 10-15 vardagar
1 416 kr
Läs direkt efter köp
550 kr
Skickas inom 10-15 vardagar
712 kr
Läs direkt efter köp
1 785 kr
Läs direkt efter köp
1 672 kr
Läs direkt efter köp
1 125 kr
Skickas inom 10-15 vardagar
1 346 kr
Skickas inom 10-15 vardagar
1 367 kr
Läs direkt efter köp
566 kr
Skickas inom 10-15 vardagar
693 kr
Läs direkt efter köp
566 kr
Skickas inom 10-15 vardagar
734 kr
Läs direkt efter köp
1 310 kr
Skickas inom 10-15 vardagar
566 kr
Skickas inom 10-15 vardagar
734 kr
Läs direkt efter köp
566 kr
Skickas inom 10-15 vardagar
Synergies of Soft Computing and Statistics for Intelligent Data Analysis
2 243 kr
Skickas inom 10-15 vardagar
2 822 kr
Läs direkt efter köp
In recent years there has been a growing interest to extend classical methods for data analysis.The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance.Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled.About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS).
This book gathers contributions presented at the SMPS''2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis.It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics.Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.