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Titlebook: Legal Language and Business Communication; Anurag K. Agarwal Book 2019 The Editor(s) (if applicable) and The Author(s), under exclusive li

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發(fā)表于 2025-3-21 18:37:05 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Legal Language and Business Communication
編輯Anurag K. Agarwal
視頻videohttp://file.papertrans.cn/584/583739/583739.mp4
概述Successfully combines legal language and business communication.Presents legal language from a business leader’s perspective.Shows readers how to be discreet in business communication
圖書封面Titlebook: Legal Language and Business Communication;  Anurag K. Agarwal Book 2019 The Editor(s) (if applicable) and The Author(s), under exclusive li
描述.This book discusses the proper use of legal language in business communication. While communicating, a business leader has to bear in mind the relevant legal framework, and be sure to never violate it. However, legal language in itself can be so complex and difficult that it is often unclear as to what meaning can be ascribed to different words and phrases used in a particular context. Also, while it’s easy to say that there are certain limits to the law, those limits are not readily visible to the uninitiated; occasionally, even experts flounder. Exploring precisely these topics, the book will be of interest to students of business, law, and business communication; managers; lawyers; researchers; practitioners; and general readers alike..
出版日期Book 2019
關(guān)鍵詞Business Communication; Interpretation; Law; Leadership; Legal Language; Legal Studies
版次1
doihttps://doi.org/10.1007/978-981-13-7534-7
isbn_ebook978-981-13-7534-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapor
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發(fā)表于 2025-3-21 23:46:43 | 只看該作者
Anurag K. Agarwalpropose a top-down induction multi-relational model tree system which navigates foreign key constraints according to a divide-and-conquer strategy, derives a representation of reference objects as bags of attribute-value vectors and then, for each bag, constructs a primary instance as main responsib
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發(fā)表于 2025-3-22 03:34:00 | 只看該作者
Anurag K. Agarwal invited to submit long papers. Out of the long paper submissions, 7 were selected for the - chine Learning Journal special issue and 27 were accepted for the proceedings. In addition, two papers were nominated by Program Committee referees for the applications prize and two for the theory prize. Th
地板
發(fā)表于 2025-3-22 05:42:48 | 只看該作者
Anurag K. Agarwalhods for deriving generalized policies and finite state controllers capable of dealing with changes in the initial situation and in the number of objects. I’ll also discuss the alternative ways in which learning can be used in planning and the challenges ahead.
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發(fā)表于 2025-3-22 10:16:18 | 只看該作者
Anurag K. Agarwalural features in order to discriminate between those examples from different classes that are particularly problematic to classify. In order to avoid overfitting, this is done in a boosting framework. We are alternating AdaBoost re-weighting episodes and feature construction episodes in order to con
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發(fā)表于 2025-3-22 19:26:52 | 只看該作者
Anurag K. Agarwale prediction, a pharmacological application and mutagenicity prediction. In two of the three applications, the task is to find configurations of points with certain properties in 3D space that characterize either a binding site or drug activity: the logical part of the clause constitutes the points
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