This can improve interpretability and reduce bias.
这能够提高可解释性并减少偏差。
Secondly, incongruity is defined as possess the characteristic of highly interpretability and enjoyability.
其次，作为制笑机制，不和谐性需具备高度的可解释性和欣赏性。
Classification methods can be compared and evaluated according to the following criteria: predictive accuracy, speed, scalability, robustness and interpretability.
实际应用中对这些算法进行取舍时，可以从准确度、速度、伸缩性、强壮性和可解释性等几个方面来评价。
In the second place, because of its evident multi-interpretability, Hobbes's original text gradually lost its capacity to function as arbiter in the historical debate.
其次，由于它明显具有的多元解读能力，霍布斯的原始文本在历史研究中逐渐丧失了其充当仲裁者的功能。
The experimental results show that the proposed fuzzy classifier based on AFS theory and Genetic Algorithm has few rules, high classification rate, and good interpretability.
从实验结果可以看出将两者结合设计出的模糊分类器具有分类准确率高、模糊描述简单、规则少且易于理解等特点。
OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
OWL提供额外的词汇，并且与正式的语义一起协作，极大地改进了机器对Web内容的解释能力，超越了XML、RDF和 RDFSchema所支持的内容解释能力。
The new fuzzy classification system has some advantages as follows: (1) good interpretability, (2) efficient feature compression, (3) comparative accuracy to the traditional methods.
新模糊分类系统具有以下优点：(1)可解释性好，(2)有效的特征压缩，(3)与传统方法相当的识别精度。
The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.
该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。
Research on the interpretability has not got enough attention. This paper proposes a method of interpretable modeling based on fuzzy clustering in order to improve the interpretability of fuzzy model.
本文针对模糊建模过程中，系统的可解释性得不到保证的问题，提出一种基于模糊聚类的可解释性建模方法。
Research on the interpretability has not got enough attention. This paper proposes a method of interpretable modeling based on fuzzy clustering in order to improve the interpretability of fuzzy model.
本文针对模糊建模过程中，系统的可解释性得不到保证的问题，提出一种基于模糊聚类的可解释性建模方法。
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