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Titlebook: Smooth Quasigroups and Loops; Lev V. Sabinin Book 1999 Springer Science+Business Media Dordrecht 1999 Algebraic structure.Group theory.Vol

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发表于 2025-3-21 16:46:58 | 显示全部楼层 |阅读模式
书目名称Smooth Quasigroups and Loops
编辑Lev V. Sabinin
视频video
丛书名称Mathematics and Its Applications
图书封面Titlebook: Smooth Quasigroups and Loops;  Lev V. Sabinin Book 1999 Springer Science+Business Media Dordrecht 1999 Algebraic structure.Group theory.Vol
描述During the last twenty-five years quite remarkable relations between nonas­ sociative algebra and differential geometry have been discovered in our work. Such exotic structures of algebra as quasigroups and loops were obtained from purely geometric structures such as affinely connected spaces. The notion ofodule was introduced as a fundamental algebraic invariant of differential geometry. For any space with an affine connection loopuscular, odular and geoodular structures (partial smooth algebras of a special kind) were introduced and studied. As it happened, the natural geoodular structure of an affinely connected space al­ lows us to reconstruct this space in a unique way. Moreover, any smooth ab­ stractly given geoodular structure generates in a unique manner an affinely con­ nected space with the natural geoodular structure isomorphic to the initial one. The above said means that any affinely connected (in particular, Riemannian) space can be treated as a purely algebraic structure equipped with smoothness. Numerous habitual geometric properties may be expressed in the language of geoodular structures by means of algebraic identities, etc.. Our treatment has led us to the purel
出版日期Book 1999
关键词Algebraic structure; Group theory; Volume; algebra; differential geometry
版次1
doihttps://doi.org/10.1007/978-94-011-4491-9
isbn_softcover978-94-010-5921-3
isbn_ebook978-94-011-4491-9
copyrightSpringer Science+Business Media Dordrecht 1999
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发表于 2025-3-21 20:17:15 | 显示全部楼层
Lev V. Sabinine do not know the correct value for the dependent variable, as we would for a supervised learning problem, but we do have an objective function called a reward. We want our machine learning model output to maximize the reward given its inputs. Additionally, the model might need to produce a series o
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merging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates th
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vey.Create an interactive web dashboard and data warehousing.We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classificationhave been proven successful. MATLAB and Python code was generated to monitor alltypes of noise pollutio
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Lev V. Sabinin for their potential in indoor localization. Since GPS signal has limited access in indoor environments, alternative sensing solutions have been employed, among which the integration of inertial measurement and WiFi Received Signal Strength (RSS) is the preferred choice for its low cost and pedestri
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