Files
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Course
Machine Learning
Description
This presentation explores how to make the best of models impacted by bias and variance. Meta-learning minimizes loss. Ensemble methods, including Bagging, Adaboost, Random Forest, Gradient Boosting, and Stacking, are discussed. These methods perturb data (X or Y) using techniques like bootstrap sampling, k-fold sampling, weighted sampling, and random subspaces. Models are generated in parallel or sequentially, with aggregation strategies such as mean, mode, weighted response, and metamodel. The presentation also touches upon deep learning and one-shot learning, and explains how distances become less meaningful in high dimensions.
More details: https://events.vtools.ieee.org/m/315184
Video Recording: https://ieeetv.ieee.org/video/meta-algorithms-in-machine-learning
Publication Date
Spring 5-31-2022
Document Type
Presentation
Keywords
Machine Learning, Artificial Intelligence, Ensemble Methods, daboost, Random Forest, Gradient Boosting, Stacking, Perturb data, Bootstrap sampling, K-fold sampling, Weighted sampling, Random subspaces, Parallel models, Sequential models, Aggregation strategies, Mean, Mode, Weighted response, Metamodel, Deep learning, One-shot learning, Distances, High dimensions
Disciplines
Computational Engineering | Computer Engineering | Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Pendyala, Vishnu, "Meta-algorithms in Machine Learning" (2022). Open Educational Resources. 10.
https://scholarworks.sjsu.edu/oer/10
