OPAC

検索結果一覧に戻る

Advances in Big Data Analytics : Theory, Algorithms and Practices / by Yong Shi

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2022
大きさ	XIV, 728 p. 1 illus : online resource
著者標目	*Shi, Yong author SpringerLink (Online service)

所蔵情報を非表示

URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002761	Computer Scinece R0 2005-6,2022-3				9789811636073

書誌詳細を非表示

一般注記	Part One: Concept and Theoretical Foundation -- Chapter 1: Big Data and Big Data Analytics -- Chapter 2: Multiple Criteria Optimization Classification -- Chapter 3: Support Vector Machine Classification -- Part Two: Functional Analysis -- Chapter 4: Feature Selection -- Chapter 5: Data Stream Analysis -- Chapter 6: Learning Analysis -- Chapter 7: Sentiment Analysis -- Chapter 8: Link Analysis -- Chapter 9: Evaluation Analysis -- Part Three: Application and Future Analysis -- Chapter 10: Business and Engineering Applications -- Chapter 11: Healthcare Applications -- Chapter 12: Artificial Intelligence IQ Test -- Chapter 13: Conclusions Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems HTTP:URL=https://doi.org/10.1007/978-981-16-3607-3
件　名	LCSH:Artificial intelligence—Data processing LCSH:Big data LCSH:Data mining LCSH:Computer science FREE:Data Science FREE:Big Data FREE:Data Mining and Knowledge Discovery FREE:Models of Computation
分　類	LCC:Q336 DC23:005.7
書誌ID	EB00002149
ISBN	9789811636073