On Tue, Sep 4, 2012 at 7:07 PM, Abram Demski <[email protected]> wrote: > This paper: > > http://arxiv.org/abs/0909.0801/ > > uses a prediction framework similar (but, somewhat different) from PAQ, > plugs it into a generic planner, and gets decent results. I find it unlikely > that PAQ would do much worse in the same experimental set-up. (I find it > likely that PAQ would do just a bit better... could be wrong, though.)
I would expect PAQ to do better, but it depends on the test environment. In the paper they use CTW, which is described here: http://mattmahoney.net/dc/dce.html#Section_423 CTW does well on stationary sources but poorly when it has to adapt to changing statistics. For example, given a bit sequence 0000000001 observed in some context, then CTW would assign P(1) = 1.5/11 (by adding 0.5 to the counts). PAQ uses indirect context models, essentially counting 0s and 1s that occurred when the same sequence was observed in other contexts. If you have 2 different file types, such as a text file and an image file, then CTW will compress them better separately than together. For PAQ it won't make much difference. CTW and PAQ both use adaptive weighted averaging of log-likelihood bit predictions. However, CTW is restricted to mixing in a chain from high to low order contexts with a separate weight associated with each lower order context. PAQ has no restrictions on the types and the order of contexts that can be mixed. It associates weights not with contexts but with models or with models plus a small context separate from the ones being mixed. Since there are fewer weights, they can be more accurate because they can be adjusted in small increments. -- Matt Mahoney, [email protected] ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
