Learning Under Distribution Mismatch
Sirvan Khalighi
Broschiertes Buch

Learning Under Distribution Mismatch

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Independent and identically and distribution (i.i.d) assumption is a basis for classical learning methods, where the test data instances follow the same probability distribution as the training instances. However, the data evolve over time and change from one domain to another, thus the training data may be outdated and not enough representatives for the distribution of the test data. Transfer learning and domain adaptation, which is a general subfield of machine learning, aims to identify, extract and transfer the relevant useful knowledge from one or more source domain/task for learning in a...