论文标题
用于探索空间负担模式的机器学习
Machine Learning for Exploring Spatial Affordance Patterns
论文作者
论文摘要
该论文使用受监督和无监督的数据挖掘技术来分析办公室平面图，以便更好地了解其几何形状到功能关系。在对自动平面生成工具的最先进的背景审查的背景审查表明，自1960年代以来已经对此类工具进行了原型，但是它们的搜索空间不明显，因为几乎没有形式主义来描述空间负担。为了显示和评估几何和使用的关系，使用视觉图分析的数据训练三个监督学习者，并将其与Zeror分类器建立的基线精度进行比较。这表明，对于检查的办公室数据集，视觉平均深度和集成最紧密地链接到使用情况，并且有监督的学习算法J48可以正确预测未见示例中的班级表现，最高为79.5％。该论文还包括对无监督的学习者对布局案例研究的评估，该研究表明，无法立即基于VGA信息来立即对其进行逆转工程，以实现强大的集群到类别的评估。
This dissertation uses supervised and unsupervised data mining techniques to analyse office floor plans in an attempt to gain a better understanding of their geometry-to-function relationship. This question was deemed relevant after a background review of the state-of-the-art in automated floor-plan generation tools showed that such tools have been prototyped since the 1960s, but their search space is ill-informed because there are few formalisms to describe spatial affordance. To show and evaluate the relationship of geometry and use, data from visual graph analysis were used to train three supervised learners and compare these to a baseline accuracy established with a ZeroR classifier. This showed that for the office dataset examined, visual mean depth and integration are most tightly linked to usage and that the supervised learning algorithm J48 can correctly predict class performance on unseen examples to up to 79.5%. The thesis also includes an evaluation of the layout case studies with unsupervised learners, which showed that use could not be immediately reverse-engineered based solemnly on the VGA information to achieve a strong cluster-to-class evaluation.
