Optimizing the inductive learning of categories and concepts: Drawing on bill Battig's principle of creating 'contextual interference' to enhance learning

2014 ◽  
Author(s):  
Robert A. Bjork ◽  
Aaron Richmond
Keyword(s):  
Inductive Learning ◽  
Contextual Interference

Transfer of Interleaving Benefits in the Inductive Learning of Categories

2011 ◽  
Author(s):  
Monica S. Birnbaum ◽  
Robert A. Bjork ◽  
Elizabeth Ligon Bjork
Keyword(s):  
Inductive Learning

How should exemplars be sequenced in inductive learning? Empirical evidence versus learners’ opinions.

2017 ◽  
Vol 23 (4) ◽  
pp. 403-416 ◽  
Author(s):  
Veronica X. Yan ◽  
Nicholas C. Soderstrom ◽  
Gayan S. Seneviratna ◽  
Elizabeth Ligon Bjork ◽  
Robert A. Bjork
Keyword(s):  
Empirical Evidence ◽  
Inductive Learning

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

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