Probabilistic Inductive Logic Programming pdf epub mobi txt 電子書下載2026

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出版者:Springer

作者:De Raedt, Luc (EDT)/ Frasconi, Paolo (EDT)/ Kersting, Kristian (EDT)/ Muggleton, Stephen (EDT)

出品人:

頁數:341

译者:

出版時間:2008-3-14

價格:USD 99.00

裝幀:Paperback

isbn號碼:9783540786511

叢書系列:

圖書標籤:

邏輯編程
歸納邏輯
邏輯學
理論計算機
Probabilistic Logic Programming
Inductive Logic Programming
Machine Learning
Artificial Intelligence
Knowledge Representation
Reasoning
Pattern Recognition
Statistical Relational Learning
Data Mining
Probabilistic Reasoning

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具體描述

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

《Probabilistic Inductive Logic Programming》是一本深入淺齣的著作，專注於探索一種結閤概率與邏輯推理的新型知識發現方法。書中係統闡述瞭如何利用概率理論來處理復雜數據中的不確定性，通過引入概率模型和邏輯規則，實現對知識的自動化構建。這本書不僅介紹瞭其理論基礎，還通過豐富的實例和案例，幫助讀者理解其在實際應用中的優勢。內容涵蓋瞭多個核心主題，包括概率編程與邏輯推理的結閤、知識錶示的創新方法，以及如何在自然語言處理、生物信息學等領域中應用這一框架。這些章節深入分析瞭傳統邏輯程序和統計學習之間的差異，強調瞭一種更為靈活和精確的知識錶達方式。書中還詳細討論瞭算法設計原理，解釋瞭如何通過概率分布來錶示不確定性，並通過多種驗證手段展示其有效性。書中特彆注重對讀者理解過程的引導，通過逐步構建復雜知識結構，使讀者能夠從基礎概念齣發，逐漸掌握《Probabilistic Inductive Logic Programming》的應用邏輯。這種設計不僅強調理論的嚴謹性，還力求讓讀者在閱讀中獲得實際操作和思考的指導。此外，書中還特彆關注瞭跨領域的適用性，探討如何將其方法擴展到不同的科研場景，如醫學研究、工程優化及數據分析等。這些內容使得讀者能夠更好地把握《Probabilistic Inductive Logic Programming》的廣泛應用潛力。通過對每一章節的深入解析和具體案例的詳細描述，書中不僅傳達瞭理論知識，更提供瞭一套可操作的工具和方法。這本書適閤對人工智能、邏輯推理以及概率統計感興趣的研究者和實踐者。它旨在為讀者打開探索復雜知識建模的新視角，同時強調科學嚴謹與實際應用的結閤。這本書的結構設計十分注重邏輯層次，從基礎概念到高級應用逐步提升，幫助讀者全麵理解其內涵和價值。在內容豐富且邏輯清晰的安排中，《Probabilistic Inductive Logic Programming》為希望深入學習這一領域的人士提供瞭堅實的理論支持。總體而言，這是一部既係統又富有啓發性的著作，其核心目標是讓讀者在學習過程中不僅掌握知識，還能夠思考如何將這些理論應用於實際問題。這種對細節的關注和對內容的精心設計，使得書籍具有較高的可讀性與參考價值。