Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (SpringerBriefs in Computer Science)

This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.

這本SpringerBrief書籍探討了分析多關聯和噪聲數據的挑戰，並提出了幾種統計關聯學習（SRL）方法。這些方法結合了一階邏輯的表達能力和概率理論處理不確定性的能力。它提供了方法的概述和關鍵假設，使其能夠適應不同的模型和現實世界應用。這些模型由於其簡潔性和可理解性而非常有吸引力，但學習它們的結構需要大量的計算。為了解決這個問題，作者們回顧了使用功能梯度來提升統計關聯模型的結構和參數。這些算法已成功應用於多個SRL場景，並已適應於從文本信息提取到醫學問題等多個實際問題。《從基準到數據驅動醫學的統計關聯學習提升》旨在為機器學習和數據挖掘的研究人員和專業人士提供上下文和經過良好測試的應用。對統計、數據管理或健康信息學感興趣的計算機工程師或學生也會發現這本簡明的資源非常有價值。