Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics

author: Jun (Luke) Huan, Department of Electrical Engineering and Computer Science, University of Kansas
published: Oct. 9, 2017,   recorded: August 2017,   views: 1056

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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.

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Reviews and comments:

Comment1 mallochio, March 12, 2018 at 8:15 p.m.:

Lecture seems to be broken.

Comment2 Eric Butler, November 29, 2018 at 3:46 a.m.:

I want to learn deep learning.

Comment3 Na ta, May 29, 2019 at 11:57 a.m.:

The information you share is very interesting, thank you very much.

Comment4 na ta, May 29, 2019 at 11:58 a.m.:

Thanks for the helpful information you shared.

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