| 書目名稱 | Learning and Intelligent Optimization |
| 副標題 | Second International |
| 編輯 | Vittorio Maniezzo,Roberto Battiti,Jean-Paul Watson |
| 視頻video | http://file.papertrans.cn/583/582901/582901.mp4 |
| 叢書名稱 | Lecture Notes in Computer Science |
| 圖書封面 |  |
| 描述 | This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8–12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge |
| 出版日期 | Conference proceedings 2008 |
| 關鍵詞 | Artificial intelligence; algorithms; combinatorial optimization; learning; machine learning; optimization |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-92695-5 |
| isbn_softcover | 978-3-540-92694-8 |
| isbn_ebook | 978-3-540-92695-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 |
| issn_series | 0302-9743 |
| copyright | Springer-Verlag Berlin Heidelberg 2008 |
|Archiver|手機版|小黑屋|
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