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[null,"1.","Record Nr.",null,"UNINA990002814590403321"]
[null,null,"Autore",null,"Pispisa, Lorenzo"]
[null,null,"Titolo",null,"Le scelte economiche nella tecnica commerciale : appunti per il Corso di tecnica industriale e commerciale della Facoltà di economia e commercio dell'Università di Pisa / Lorenzo Pispisa"]
[null,null,"Pubbl/distr/stampa",null,"Pisa, : Tecnica Scientifica, 1961"]
[null,null,"Edizione",null,"[2. ed]"]
[null,null,"Descrizione fisica",null,"285 p. ; 25 cm"]
[null,null,"Disciplina",null,"658.8"]
[null,null,"Locazione",null,"ECA"]
[null,null,"Collocazione",null,"3-6-TB"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910678055903321"]
[null,null,"Autore",null,"Flood Joanne M."]
[null,null,"Titolo",null,"Wiley GAAP 2019 : interpretation and application of generally accepted accounting principles. / / Joanne M. Flood"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : Wiley, , 2019"]
[null,null,"ISBN",null,"1-119-51160-7"]
[{"$nulls":4},"1-119-57553-2"]
[{"$nulls":4},"1-119-51159-3"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (1,443 pages)"]
[null,null,"Disciplina",null,"657.021873"]
[null,null,"Soggetti",null,"Accounting - Standards - United States"]
[{"$nulls":4},"Comptabilitat"]
[{"$nulls":4},"Normes"]
[{"$nulls":4},"Llibres electrònics"]
[{"$nulls":4},"Estats Units d'Amèrica"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","The most comprehensive guide to FASB Codifications, updated with the latest pronouncements Wiley GAAP 2019 is the essential resource for US GAAP implementation. Covering all codifications by the Financial Accounting Standards Board (FASB)—including the latest updates—this book provides clear explanations and practical examples for real-world application of these dynamic guidelines. Each chapter includes relevant sources of GAAP and expert guidance on interpretation, terminology, relevant concepts, and applicable rules, while in-depth discussion on the issues surrounding specific pronouncements offers informative perspective for a variety of scenarios. This user-friendly reference covers every pronouncement currently in effect or being deliberated—including FASB Technical Bulletins, FASB Implementation Guides, AcSEC Practice Bulletins, and AICPA Accounting Interpretations—in a single volume, fully referenced to the FASB Current Text and cross-referenced to the new FASB codification system. Clear and concise without sacrificing depth or rigor, this invaluable resource simplifies research and helps CPAs and other accounting professionals ensure accuracy and compliance. Examine the latest changes to US GAAP standards and "]
[{"$nulls":4},"practices Gain expert perspectives on the issues surrounding specific pronouncements Learn how the standards translate to common real-world scenarios Clarify implementation through numerous illustrations and real-world examples Staying up-to-date with constantly-evolving guidelines is a challenge, but the requirement for accurate interpretation and appropriate application adds an additional layer of complexity in an area where noncompliance could expose an organization to significant risk. Wiley GAAP 2019 provides the guidance, insight, and perspective accounting professionals need to ensure accurate and up-to-date GAAP implementation."]
[null,"3.","Record Nr.",null,"UNINA9910350230503321"]
[null,null,"Autore",null,"Zhou Zhi-Hua"]
[null,null,"Titolo",null,"Evolutionary Learning: Advances in Theories and Algorithms / / by Zhi-Hua Zhou, Yang Yu, Chao Qian"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019"]
[null,null,"ISBN",null,"981-13-5956-3"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XII, 361 p. 59 illus., 20 illus. in color.)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer science—Mathematics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Math Applications in Computer Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. ."]
[null,null,null,"Sommario/riassunto","Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, "]
[{"$nulls":4},"divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. ."]