| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910701876303321 |
|
|
Titolo |
2011 tax filing [[electronic resource] ] : processing gains, but taxpayer assistance could be enhanced by more self-service tools : report to congressional requesters |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
[Washington, D.C.] : , : U.S. Govt. Accountability Office, , [2011] |
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (iii, 54 pages) : color illustrations, color map |
|
|
|
|
|
|
Soggetti |
|
Tax administration and procedure - United States |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Title from PDF title screen (viewed May 9, 2012). |
"December 2011." |
"GAO-12-176." |
|
|
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910522560303321 |
|
|
Titolo |
Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II / / edited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2022.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (571 pages) |
|
|
|
|
|
|
Collana |
|
Information Systems and Applications, incl. Internet/Web, and HCI, , 2946-1642 ; ; 13164 |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Algorithms |
Application software |
Numerical analysis |
Computer networks |
Social sciences - Data processing |
Artificial Intelligence |
Design and Analysis of Algorithms |
Computer and Information Systems Applications |
Numerical Analysis |
Computer Communication Networks |
Computer Application in Social and Behavioral Sciences |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Deep Learning -- Machine Learning -- Reinforcement Learning -- Neural Networks -- Deep Reinforcement Learning -- Optimization -- Global Optimization -- Multi-Objective Optimization -- Computational Optimization -- Data Science -- Big Data -- Data Analytics -- Artificial Intelligence. |
|
|
|
|
|
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications. |
|
|
|
|
|
|
|
| |