Supervised Machine Learning: Optimization Framework and Applications with SAS and RКНИГИ » ПРОГРАММИНГ
Название: Supervised Machine Learning: Optimization Framework and Applications with SAS and R Автор: Tanya Kolosova, Samuel Berestizhevsky Издательство: Chapman and Hall/CRC Год: 2021 Формат: PDF, EPUB Страниц: 176 Размер: 10 Mb Язык: English
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers.
Key Features:
Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data
Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments
Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias
Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks
Computer programs in R and SAS that create AI framework are available on GitHub
Уважаемый посетитель, Вы зашли на сайт как незарегистрированный пользователь.
С этой публикацией часто скачивают:
How Machine Learning Works (MEAP) Название: How Machine Learning Works (MEAP) Автор: Mostafa Samir Abd El-Fattah Издательство: Manning Publications Год: 2020 Страниц: 220 Язык:...
Data Classification. Algorithms and Applications Название: Data Classification. Algorithms and Applications Автор: Charu Aggarwal Издательство: Chapman and Hall Год: 2014 Формат: pdf Страниц: 705...
Навигация
Вход на сайт
Реклама
Supervised Machine Learning: Optimization Framework and Applications with SAS and R 
