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|Title:||On the data consumption benefits of accepting increased uncertainty|
Long term memory
|Citation:||Martin, E.,Sharma, A.,Stephan, F. (2004). On the data consumption benefits of accepting increased uncertainty. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 3244 : 83-98. ScholarBank@NUS Repository.|
|Abstract:||In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret 'desirable' as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds - for hypotheses and for data - is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability. © Springer-Verlag Berlin Heidelberg 2004.|
|Source Title:||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Appears in Collections:||Staff Publications|
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