| 書(shū)目名稱(chēng) | Inductive Logic Programming |
| 副標(biāo)題 | 30th International C |
| 編輯 | Nikos Katzouris,Alexander Artikis |
| 視頻video | http://file.papertrans.cn/464/463909/463909.mp4 |
| 叢書(shū)名稱(chēng) | Lecture Notes in Computer Science |
| 圖書(shū)封面 |  |
| 描述 | This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually.. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.. |
| 出版日期 | Conference proceedings 2022 |
| 關(guān)鍵詞 | artificial intelligence; computer programming; computer science; computer systems; data mining; education |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-97454-1 |
| isbn_softcover | 978-3-030-97453-4 |
| isbn_ebook | 978-3-030-97454-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 |
| issn_series | 0302-9743 |
| copyright | Springer Nature Switzerland AG 2022 |
|Archiver|手機(jī)版|小黑屋|
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