A machine learning model with human cognitive biases capable of learning from small and biased datasets
Abstract Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce...
Main Authors: | Hidetaka Taniguchi, Hiroshi Sato, Tomohiro Shirakawa |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2018-05-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-018-25679-z |
Similar Items
-
Implementation of Human Cognitive Bias on Naïve Bayes
by: Hidetaka Taniguchi, et al.
Published: (2016-05-01) -
Machine-learning media bias
by: D'Alonzo, Samantha, et al.
Published: (2022) -
Not all biases are bad: equitable and inequitable biases in machine learning and radiology
by: Mirjam Pot, et al.
Published: (2021-02-01) -
Detection and Evaluation of Machine Learning Bias
by: Salem Alelyani
Published: (2021-07-01) -
Cognitive bias and learning from experience: Reflective processes for reducing bias
by: Acklin, Dina
Published: (2015)