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The Influence Of Corporate Law And Accounting Principles In Determining Taxable Income : Proceedings Of A Seminar Held In Geneva In 1996 During The 50th Congress Of The International Fiscal Association

RRP $338.99

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The interaction between accounting, company law, and taxation is a key issue in corporate regulation. These three areas have differing kinds of regulations with different objectives and varied techniques of regulation and interpretation. Moreover, views on all of these factors vary from country to country.How these fields interact has significant ramifications. It affects the future development of both accounting regulation and tax law and raises the possibility of international double taxation. These potential effects suggest the need not only for better analysis in this area but also for a harmonised international view. The Influence of Corporate Law and Accounting Principles in Determining Taxable Income takes a thoughtful look at several components of this key debate. The papers included offer insights into the different paths taken on the various issues by selected countries, cites problems, and offers proposals for the future. Tax and corporate law practitioners, policymakers, and academics will appreciate the insightful and thought-provoking quality of this work.


Data Mining And Modeling Of Hiv/aids Dynamics

RRP $285.99

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The first book to integrate HIV biological and clinical topics with data mining techniques, this volume offers a comprehensive, multidisciplinary source of information for clinicians, biologists, and computational scientists. It addresses problems at the intersection of HIV biology and evolution, clinical and medical problems, and computational methods for mining and modeling HIV/AIDS biological and clinical data. Key features include databases and data management and analysis tools, plus several case studies and their solutions using real datasets collected over several years from actual HIV infected individuals.


Automating The Design Of Data Mining Algorithms

RRP $55.99

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Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.



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