Learning Probabilistic Relational Concepts

小行星9310413 所提出的研究的目的是调查的实用程序的算法，学习概率关系的概念，从嘈杂的关系数据，集中在算法，使用证据组合进行分类。 特别令人感兴趣的是，如果考虑到规则的可靠性，则可以获得分类准确性的潜在改进。 另一个目标是扩展噪声容忍学习算法，以利用不完整或不确定的知识的概念来学习。 这样的知识可以表示为部分不正确或不完整的域理论。 这些算法将在UCI机器学习数据库的一些数据集上进行测试，并在三个新的关系数据集上进行测试，这些数据集涉及脊髓损伤识别，矿物光谱识别和漏油分析。 此外，它们将在一些合成域上进行测试，其中参数的变化将允许洞察这些算法的一般行为。 HYDROGEN系统 (Ali Pazzani，1993年），它已经显示了一些承诺，对有限的数据集将显着扩展这些调查。

9310413 Pazzani The objective of the proposed research are to investigate the utility of algorithms that learn probabilistic relational concepts from noisy, relational data, concentrating on algorithms that perform classification using evidence combination. Of particular interest is the potential improvement in classification accuracy that can be obtained if the reliability of rules is taken into account. An additional objective is to extend noise-tolerant learning algorithms to take advantage of incomplete or uncertain knowledge of the concept to be learned. Such knowledge may be expressed as a partially incorrect or incomplete domain theory. These algorithms will be tested on a number of data sets from the UCI Repository of Machine Learning data sets and on three new relational data sets involving spinal injury identification, mineral spectra identification, and oil spill analysis. In addition, they will be tested on a number of synthetic domains, where variation of parameters will allow insight into the general behavior of these algorithms. The system HYDRA (Ali & Pazzani, 1993), which has already shown some promise on a limited number of data sets will be significantly extended for these investigations.