COIN CON ;) On Tue, Jul 7, 2020 at 9:22 AM James Bowery <jabow...@gmail.com> wrote: > > A slight modification to avoid idioms like "the HIV virus": > COINC = COIN Criterion > COIN = COmpression Information criteriON > > Otherwise it would be good memetics if its psychological appeal vs memetic > drift reaches the selective regime in time to achieve fixation against the > psychological appeal of the *IC prior. > > On Tue, Jul 7, 2020 at 1:42 AM Ben Goertzel <b...@goertzel.org> wrote: >> >> The COIN Criterion ... sounds like money, it's got to be good... >> >> On Mon, Jul 6, 2020 at 9:13 PM James Bowery <jabow...@gmail.com> wrote: >> > >> > On Fri, Jul 3, 2020 at 6:53 PM Ben Goertzel <b...@goertzel.org> wrote: >> >> >> >> ...Under what conditions is it the case that, for prediction based on a >> >> dataset using realistically limited resources, the smallest of the >> >> available programs that precisely predicts the training data actually >> >> gives the best predictions on the test data? >> > >> > >> > If I may refine this a bit to head off misunderstanding at the outset of >> > this project: >> > >> > The CIC* (Compression Information Criterion) hypothesis is that among >> > existing models of a process producing an executable archive of the same >> > training data within the same computation constraints, the one that >> > produces the smallest executable archive will in general be the most >> > accurate on the test data. >> > >> > >> > Run a number of experiments and for each: >> > 1 Select a nontrivial >> > 1.1 computational resource level as constraint >> > 1.2 real world dataset -- no less than 1GB gzipped. >> > 2 Divide the data into training and testing sets >> > 3 For each competing model: >> > 3.1 Provide the training set >> > 3.2 Record the length of the executable archive the model produces >> > 3.3 Append the test set to the training set >> > 3.4 Record the length of the executable archive the model produces >> > 4 Produce 2 rank orders for the models >> > 4.1 training set executable archive sizes >> > 4.2 training with testing set executable archive sizes >> > 5 Record differences in the training vs test rank orders >> > >> > The lower the average differences the more general the criterion. >> > >> > It should be possible to run similar tests of other model selection >> > criteria and rank order model selection criteria. >> > >> > *We're going to need a catchy acronym to keep up with: >> > >> > AIC (Akaike Information Criterion) >> > BIC (Bayesian Information Criterion)... >> > ...aka >> > SIC (Schwarz Information Criterion)... >> > ...aka >> > MDL or MDLP (both travestic abuses of "Minimum Description Length >> > [Principle]" that should be forever cast into the bottomless pit) >> > HQIC (Hannan-Quinn Information Criterion)... >> > KIC (Kullback Information Criterion) >> > etc. etc. >> > Artificial General Intelligence List / AGI / see discussions + >> > participants + delivery options Permalink >> >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> “The only people for me are the mad ones, the ones who are mad to >> live, mad to talk, mad to be saved, desirous of everything at the same >> time, the ones who never yawn or say a commonplace thing, but burn, >> burn, burn like fabulous yellow roman candles exploding like spiders >> across the stars.” -- Jack Kerouac > > Artificial General Intelligence List / AGI / see discussions + participants + > delivery options Permalink
-- Ben Goertzel, PhD http://goertzel.org “The only people for me are the mad ones, the ones who are mad to live, mad to talk, mad to be saved, desirous of everything at the same time, the ones who never yawn or say a commonplace thing, but burn, burn, burn like fabulous yellow roman candles exploding like spiders across the stars.” -- Jack Kerouac ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta901988932dbca83-M58ea1fc2de53183a853f3b1b Delivery options: https://agi.topicbox.com/groups/agi/subscription
