Reasoning Web. Causality, Explanations and Declarative Knowledge : 18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial Lectures (Lecture Notes in Computer Science)

個数：

Reasoning Web. Causality, Explanations and Declarative Knowledge : 18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial Lectures (Lecture Notes in Computer Science)

Bertossi, Leopoldo (EDT)/ Xiao, Guohui (EDT)

ウェブストア価格 ¥13,633（本体¥12,394）
Springer International Publishing AG（2023/04発売）
外貨定価 US$ 64.99
ポイント 123pt

提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
【重要ご説明事項】
1. 納期遅延や、ご入手不能となる場合が若干ございます。
2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
3. 美品のご指定は承りかねます。

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 211 p.
言語 ENG
商品コード 9783031314131
DDC分類 025.0427

Full Description

The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers.
The broad theme of this year's summer school was "Reasoning in Probabilistic Models and Machine Learning" and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications.
The following eight lectures were presented during the school: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.

Explainability in Machine Learning.- Causal Explanations and Fairness in Data.- Statistical Relational Extensions of Answer Set Programming.- Vadalog: Its Extensions and Business Applications.- Cross-Modal Knowledge Discovery, Inference, and Challenges.- Reasoning with Tractable Probabilistic Circuits.- From Statistical Relational to Neural Symbolic Artificial Intelligence.- Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.