検索結果をRefWorksへエクスポートします。対象は1件です。
Export
RT Book, Whole SR Electronic DC OPAC T1 Wineinformatics : A New Data Science Application / by Bernard Chen T2 SpringerBriefs in Computer Science. ISSN:21915776 A1 Chen, Bernard A1 SpringerLink (Online service) YR 2023 FD 2023 SP IX, 69 p. 1 illus K1 Artificial intelligence -- Data processing K1 Machine learning K1 Natural language processing (Computer science) K1 Expert systems (Computer science) K1 Business information services K1 Social sciences -- Data processing K1 Data Science K1 Machine Learning K1 Natural Language Processing (NLP) K1 Knowledge Based Systems K1 Business Information Systems K1 Computer Application in Social and Behavioral Sciences ED 1st ed. 2023. PB Springer Nature Singapore : Imprint: Springer PP Singapore SN 9789811973697 LA English (英語) CL LCC:Q336 CL DC23:005.7 NO Chapter 1 Introduction -- Chapter 2 Data collection and preprocessing -- Chapter 3 Classification in Wineinformatics -- Chapter 4 Regression on Wine Prediction -- Chapter 5 Analysis on Wine Reviewers -- Chapter 6 Advanced Application of the Computational Wine Wheel -- Chapter 7 Conclusion and Future Works NO Wineinformatics is a new data science application with a focus on understanding wine through artificial intelligence. Thousands of new wine reviews are produced monthly, which benefits the understanding of wine through wine experts for winemakers and consumers. This book systematically investigates how to process human language format reviews and mine useful knowledge from a large volume of processed data. This book presents a human language processing tool named Computational Wine Wheel to process professional wine reviews and three novel Wineinformatics studies to analyze wine quality, price and reviewers. Through the lens of data science, the author demonstrates how the wine receives 90+ scores out of 100 points from Wine Spectator, how to predict a wine’s specific grade and price through wine reviews and how to rank a group of wine reviewers. The book also shows the advanced application of the Computational Wine Wheel to capture more information hidden in wine reviews and the possibility of extending the wheel to coffee, tea beer, sake and liquors. This book targets computer scientists, data scientists and wine industrial researchers, who are interested in Wineinformatics. Senior data science undergraduate and graduate students may also benefit from this book NO HTTP:URL=https://doi.org/10.1007/978-981-19-7369-7 NO 書誌ID=EB00001155; LK [E Book]https://doi.org/10.1007/978-981-19-7369-7 OL 30