建构主义学习:透明预测分析的学习范式Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics |
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课程网址: | http://videolectures.net/kdd2017_huan_constructivism_learning/ |
主讲教师: | Jun (Luke) Huan |
开课单位: | 堪萨斯大学 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 开发透明的预测分析最近引起了广泛的研究关注。关于如何对学习透明度进行建模有多种理论,但没有一种理论旨在理解内部且通常很复杂的建模过程。在本文中,我们采用了一个称为“建构主义”的当代哲学概念,这是一种关于人类如何学习的理论。我们假设透明机器学习的一个关键方面是通过两个关键过程“揭示”模型构建:(1)我们增强现有学习模型的同化过程和(2)我们创建新学习模型的适应过程。凭借这种直觉,我们提出了一种新的学习范式,使用贝叶斯非参数来动态处理新学习任务的创建。 |
课程简介: | Developing transparent predictive analytics has attracted significant research attention recently. There have been multiple theories on how to model learning transparency but none of them aims to understand the internal and often complicated modeling processes. In this paper we adopt a contemporary philosophical concept called ``constructivism’‘, which is a theory regarding how human learns. We hypothesis that a critical aspect of transparent machine learning is to ``reveal’’ model construction with two key process: (1) the assimilation process where we enhance our existing learning models and (2) the accommodation process where we create new learning models. With this intuition we propose a new learning paradigm using a Bayesian nonparametric to dynamically handle the creation of new learning tasks. Our empirical study on both synthetic and real data sets demonstrate that the new learning algorithm is capable of delivering higher quality models (as compared to base lines and state-of-the-art) and at the same time increasing the transparency of the learning process. |
关 键 词: | 透明预测; 建构主义; 贝叶斯非参数 |
课程来源: | 视频讲座网 |
数据采集: | 2023-12-26:wujk |
最后编审: | 2023-12-26:wujk |
阅读次数: | 26 |