SØG - mellem flere end 8 millioner bøger:
Viser: Veracity of Big Data - Machine Learning and Other Approaches to Verifying Truthfulness
Veracity of Big Data Vital Source e-bog
Vishnu Pendyala
(2018)
Veracity of Big Data
Machine Learning and Other Approaches to Verifying Truthfulness
Vishnu Pendyala
(2018)
Sprog: Engelsk
Detaljer om varen
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Springer Nature (Juni 2018)
- ISBN: 9781484236338
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback
- Udgiver: Apress L. P. (Juni 2018)
- ISBN: 9781484236321
Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language.
Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion.
What You'll Learn
- Understand the problem concerning data veracity and its ramifications
- Develop the mathematical foundation needed to help minimize the impact of the problem using easy-to-understand language and examples
- Use diverse tools and techniques such as machine learning algorithms, Blockchain, and the Kalman filter to address veracity issues
Software developers and practitioners, practicing engineers, curious managers, graduate students, and research scholars