یادداشتهای مربوط به کتابنامه ، واژه نامه و نمایه های داخل اثر
متن يادداشت
Includes bibliographical references
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Enginesfeatures a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's classic data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. Thismonograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinkingby considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization
ویراست دیگر از اثر در قالب دیگر رسانه
عنوان
Realtime data mining
شماره استاندارد بين المللي کتاب و موسيقي
9783319013206
قطعه
عنوان
OhioLINK electronic book center (Online)
عنوان
SpringerLink
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Data mining
مقوله موضوعی
موضوع مستند نشده
COM-- 000000
رده بندی ديویی
شماره
006
.
3/12
ويراست
23
رده بندی کنگره
شماره رده
QA76
.
9
.
D343
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )