Results 231 to 240 of about 57,852 (256)

Interpretability in Machine Learning – Principles and Practice

2013
Theoretical advances in machine learning have been reflected in many research implementations including in safety-critical domains such as medicine. However this has not been reflected in a large number of practical applications used by domain experts.
openaire   +1 more source

Shapley variable importance cloud for interpretable machine learning

Patterns, 2022
Marcus Ong, Yilin Ning, Bibhas Chakraborty   +2 more
exaly  

Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model

Information Systems Research, 2022
Yu Jeffrey Hu, Tong Wang, Fujie Jin
exaly  

Machine Learning Interpretation Tools

2020
openaire   +1 more source

Opening the Black Box: Interpretable Machine Learning for Geneticists

Trends in Genetics, 2020
Shin-Han Shiu, Jiliang Tang, Christina Brady Del Azodi   +2 more
exaly  

New statistical learning perspective for design of a physically interpretable prototypical neural network for machine condition monitoring

Mechanical Systems and Signal Processing, 2023
Zhike Peng, Tongtong Yan, Changqing Shen   +2 more
exaly  

Interpretability in Machine Learning

2023
openaire   +1 more source

Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach

Technological Forecasting and Social Change, 2022
Changyong Lee, Juram Kim, Gyumin Lee
exaly  

A Review of Framework for Machine Learning Interpretability

2022
Ivo de Abreu Araújo, Renato Hidaka Torres, Nelson Cruz Sampaio Neto   +2 more
openaire   +1 more source

Deciphering RNA splicing logic with interpretable machine learning

Proceedings of the National Academy of Sciences of the United States of America, 2023
Mukund Sudarshan, Susan E Liao, Oded Regev   +2 more
exaly