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1. |
Record Nr. |
UNINA9910780859303321 |
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Autore |
Brannigan Michael C. <1948-> |
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Titolo |
Striking a balance [[electronic resource] ] : a primer in traditional Asian values / / Michael C. Brannigan |
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Pubbl/distr/stampa |
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Lanham, MD, : Lexington Books, 2010 |
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ISBN |
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1-282-47917-2 |
9786612479175 |
0-7391-3847-2 |
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Edizione |
[Rev. ed.] |
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Descrizione fisica |
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1 online resource (249 p.) |
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Disciplina |
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Soggetti |
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Ethics - Asia |
Religious ethics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover; Striking a Balance; Contents; Preface to Revised Edition; Acknowledgments for the Revised Edition; Introduction; CHAPTER ONE: Hindu Ethics; CHAPTER TWO: Buddhist Ethics; CHAPTER THREE: Zen Buddhist Ethics; CHAPTER FOUR: Taoist Ethics; CHAPTER FIVE: Confucian Ethics; Bibliography; Index; About the Author |
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Sommario/riassunto |
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Striking a Balance offers a lucid, thoughtful, and thoroughly engaging review of the major ethical teachings in the dominant Asian traditions. Michael C. Brannigan applies his extensive background and scholarship to craft a concise yet comprehensive introduction to Asian ethics covering the long-standing traditions of Hinduism, Buddhism, Zen Buddhism, Taoism, and Confucianism. |
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2. |
Record Nr. |
UNINA9910484827803321 |
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Autore |
Owsiński Jan W |
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Titolo |
Data Analysis in Bi-partial Perspective: Clustering and Beyond / / by Jan W. Owsiński |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIX, 153 p.) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 818 |
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Disciplina |
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Soggetti |
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Engineering—Data processing |
Computational intelligence |
Artificial intelligence |
Data Engineering |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Preface -- Chapter 1. Notation and main assumptions -- Chapter 2. The problem of cluster analysis -- Chapter 3. The general formulation of the objective function -- Chapter 4. Formulations and rationales for other problems in data analysis, etc. |
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Sommario/riassunto |
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This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations. This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the |
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bi-partial principle can be effectively applied to a wide variety of problems in data analysis. The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard “academic” manner. |
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