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- W3107777250 abstract "Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima. In this paper we advocate the teaching of alternative methods of generalising to the best possible solution, including a method called anti-learning. By using simple teaching methods, students can achieve a deeper understanding of the importance of validation on data excluded from the training process and that each problem requires its own methods to solve. We also exemplify the requirement to train a model using sufficient data by showing that different granularities of cross-validation can yield very different results." @default.
- W3107777250 created "2020-12-07" @default.
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- W3107777250 date "2018-01-01" @default.
- W3107777250 modified "2023-09-23" @default.
- W3107777250 title "Teaching Key Machine Learning Principles Using Anti-Learning Datasets" @default.
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- W3107777250 doi "https://doi.org/10.2139/ssrn.3786478" @default.
- W3107777250 hasPublicationYear "2018" @default.
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