No free lunch versus Occam's Razor in supervised learning
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|01_Lattimore_No_free_lunch_versus_Occam's_2013.pdf||227.7 kB||Adobe PDF||Request a copy|
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