Probabilistic Inductive Logic Programming pdf epub mobi txt 电子书下载 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:

页数:341

译者:

出版时间:2008-3-14

价格:USD 99.00

装帧:Paperback

isbn号码:9783540786511

丛书系列:

图书标签:

逻辑编程
归纳逻辑
逻辑学
理论计算机
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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具体描述

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》是一本深入浅出的著作，专注于探索一种结合概率与逻辑推理的新型知识发现方法。书中系统阐述了如何利用概率理论来处理复杂数据中的不确定性，通过引入概率模型和逻辑规则，实现对知识的自动化构建。这本书不仅介绍了其理论基础，还通过丰富的实例和案例，帮助读者理解其在实际应用中的优势。内容涵盖了多个核心主题，包括概率编程与逻辑推理的结合、知识表示的创新方法，以及如何在自然语言处理、生物信息学等领域中应用这一框架。这些章节深入分析了传统逻辑程序和统计学习之间的差异，强调了一种更为灵活和精确的知识表达方式。书中还详细讨论了算法设计原理，解释了如何通过概率分布来表示不确定性，并通过多种验证手段展示其有效性。书中特别注重对读者理解过程的引导，通过逐步构建复杂知识结构，使读者能够从基础概念出发，逐渐掌握《Probabilistic Inductive Logic Programming》的应用逻辑。这种设计不仅强调理论的严谨性，还力求让读者在阅读中获得实际操作和思考的指导。此外，书中还特别关注了跨领域的适用性，探讨如何将其方法扩展到不同的科研场景，如医学研究、工程优化及数据分析等。这些内容使得读者能够更好地把握《Probabilistic Inductive Logic Programming》的广泛应用潜力。通过对每一章节的深入解析和具体案例的详细描述，书中不仅传达了理论知识，更提供了一套可操作的工具和方法。这本书适合对人工智能、逻辑推理以及概率统计感兴趣的研究者和实践者。它旨在为读者打开探索复杂知识建模的新视角，同时强调科学严谨与实际应用的结合。这本书的结构设计十分注重逻辑层次，从基础概念到高级应用逐步提升，帮助读者全面理解其内涵和价值。在内容丰富且逻辑清晰的安排中，《Probabilistic Inductive Logic Programming》为希望深入学习这一领域的人士提供了坚实的理论支持。总体而言，这是一部既系统又富有启发性的著作，其核心目标是让读者在学习过程中不仅掌握知识，还能够思考如何将这些理论应用于实际问题。这种对细节的关注和对内容的精心设计，使得书籍具有较高的可读性与参考价值。