Results 271 to 280 of about 1,147,666 (317)

Model-Based Machine Learning

Journal of the American Statistical Association, 2023
He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and ...
Chris Nakas, Leonidas E Bantis, Constantine Gatsonis   +2 more
semanticscholar   +1 more source

A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences

The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012
This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML ...
Bernard De Baets, Shinji Fukuda
openaire   +2 more sources

Machine Learning in Building a Collection of Computer Science Course Syllabi

2012
Syllabi are rich educational resources. However, finding Computer Science syllabi on a generic search engine does not work well. Towards our goal of building a syllabus collection we have trained various Decision Tree, Naive-Bayes, Support Vector Machine and Feed-Forward Neural Network classifiers to recognize Computer Science syllabi from other web ...
Lillian N. Cassel, Nakul Rathod
openaire   +2 more sources

Survey of explainable machine learning with visual and granular methods beyond quasi-explanations

Studies in Computational Intelligence, 2020
This paper surveys visual methods of explainability of Machine Learning (ML) with focus on moving from quasi-explanations that dominate in ML to domain-specific explanation supported by granular visuals.
Boris Kovalerchuk, M. Ahmad, Ankur Teredesai Department of Computer Science, Central Washington University, Usa Department of Computer Science, Systems, University of Washington Tacoma, Usa Kensci Inc., Usa   +8 more
semanticscholar   +1 more source

Using Bayesian Networks and Machine Learning to Predict Computer Science Success

2018
Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links inherent in Bayesian Networks allow for understanding of the contributing factors for ...
Zachary Nudelman, Deshendran Moodley, Sonia Berman   +2 more
openaire   +2 more sources

Machine Learning for Data Science: Mathematical or Computational

2015
Machine learning usually requires getting a training and testing set of samples. The training set is used to obtain the model, and then, the testing set is used to verify the model. In general, a machine learning method requires an iterated process for reaching a goal. Machine learning is one of the research areas in artificial intelligence.
openaire   +2 more sources

Accelerating materials science with high-throughput computations and machine learning

Computational Materials Science, 2019
Abstract With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design.
openaire   +2 more sources

Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning

Journal of Science Education and Technology, 2020
This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test.
Richard Lamb, Brian Hand, Amanda Kavner
openaire   +2 more sources

Advances in machine learning- and artificial intelligence-assisted material design of steels

International Journal of Minerals, Metallurgy, and Materials, 2023
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiao-Ye Zhou, X. Mao   +9 more
semanticscholar   +1 more source

Machine learning: Trends, perspectives, and prospects

Science, 2015
Michael I. Jordan, T. Mitchell
semanticscholar   +1 more source