Computer Science – Learning
Scientific paper
2005-03-31
Journal of Computer and System Sciences, 74(4):457-489, 2008
Computer Science
Learning
49 pages, 1 figure, journal version of COLT'96 paper
Scientific paper
We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent to PFIN-type learning with the probability of success p_2. To prove our result, we analyze the topological structure of the probability hierarchy. We prove that it is well-ordered in descending ordering and order-equivalent to ordinal epsilon_0. This shows that the structure of the hierarchy is very complicated. Using similar methods, we also prove that, for PFIN-type learning, team learning and probabilistic learning are of the same power.
No associations
LandOfFree
Probabilistic and Team PFIN-type Learning: General Properties does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Probabilistic and Team PFIN-type Learning: General Properties, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Probabilistic and Team PFIN-type Learning: General Properties will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-603071