rubin stuart spawar gydugzbnktnnavymil david reply subject similarity dont believe youve seen add solomonoff kolmogorov chaitin addressed facets randomization particular solomonoffs work grammatical inference eg see ks funotes grammars cf general inherently depend application heuristics brings us full circle note prof zadeh theory heuristic acquisition copied thank stuart rubin phd sscsd tel 619 5533554 fax 619 5531130 e gydugzbnktnnavymil original message rubin stuart spawar sent friday november 30 2007 1214 pm gzrdaeecsberkeleyedu faqengrorstedu cc rubin stuart spawar shuching chen porjkahunasdsuedu witold pedrycz subject uai define similarity dear professor zadeh read statement great interest first glance reminded heinseburg uncertainty principle thus impossibility defining position subatomic particle introduces quantum mechanics allows statistical macro solution similarly atrocious pun one must define similarity small rather large believe proper metric success randomization spatial representation b search time pursuing line thought one arrives definition heuristic search acquisition heuristic definition heuristics first apparently limited mathematics selfreference inherent heuristic nature self referential process neither computability cousin learnability theory applied delimit knowledge acquired process similarity broadly defined also predicted recent paper randomization discovery furthermore one notices randomization made reference formally allow selfmodifying heuristics bolstering support contention similarity properly defined randomization large admiration respect stuart rubin phd sscsd tel 619 5533554 fax 619 5531130 e gydugzbnktnnavymil original message david l dowe mailtosgwtgbwmhinfotechmonasheduau sent sunday december 09 2007 1207 faqengrorstedu cc rubin stuart spawar jdykjnglapjsanueduau gzrdaeecsberkeleyedu subject uai define similarity hi stuart marcus peter lotfi peter mcburney raises issue equivalence two objects posting important ill sidestep address words similar similarity assuming satisfactorily represent objects binary bit strings agree marcus hutter stuart rubin appeal kolmogorov complexity equivalently algorithmic information theory determine whether objects said similar however couple caveats might perhaps order first wish know whether objects similar also want theory inductive inference hypothesising explaining similarity latter event actually wanting theory inductive inference hypothesis model twopart encoding encoding hypothesis h data given hypothesis h rather onepart coding relevant technical details fine distinction given wallace dowe 1999a minimum message length kolmogorov complexity computer j vol 42 pp270283 wwwcssemonasheduaudlddaviddowepublicationshtmlwallacedowe1999 secs 22 23 c wallace 2005 statistical inductive inference minimum message length wwwcssemonasheduaudldcswallacepublicationsmmlbook practice difference onepart codelength twopart mml codelengths small occasionally rarely two onepart twopart codelengths equal uncommon difference code lengths much less sizes discarded terms used authors relevant approximations like provide pointers literature minimum message length mml applied similarity eg languages dna strings using twopart mml encoding enables us model inferred similarities marcus hutter also mentioned identical surely similar literature second caveat worth mentioning bayesianism appealing work solomonoff kolmogorov andor chaitin algorithmic information theory subject bayesian choice universal turing machine utm two different utms t1 t2 require finite translation constant ct1 t2 get one emulation size constant ct1 t2 could practice large compared sizes datasets study mml likewise bayesian one published place one find discussion efforts reduce degree subjectivity via size constant ct1 t2 chapter 2 wallace 2005 last probably least youre looking light introductory 46page read mml written philosophers largely philosophers please feel welcome try dowe gardner oppy brit j philos sci december 2007 pp709754 bayes bust simplicity problem bayesians wwwcssemonasheduaudlddaviddowepublicationshtmldowegardneroppy 2007 hope clear trust hope helps thanks reading cheers david mon dec 03 2007 104753am 0800 rubin stuart spawar wrote prof hutter make excellent points pursuant chaitins randomization kolmogorov complexity course synonymous one difference however alluded whole science underpinning evolution heuristics perform clustering classification task methods address point thank stuart rubin phd sscsd tel 619 5533554 fax 619 5531130 e gydugzbnktnnavymil original message nhipljlbixengrorstedu mailtonhipljlbixengrorstedu behalf marcus hutter sent saturday december 01 2007 1204 faqengrorstedu cc lotfi zadeh subject uai define similarity dear lofti paul vitanyi others developed universal similarity based kolmogorov complexity k general optimality properties justifies label universal applied wide range problems phylogenetic tree reconstruction similarity languages determining composer pieces music idea two objects b similar better compressed jointly separately difference compression length dabkakbkab used dissimilarity measure know candidate except variants may honestly called universal similarity agree degree similarity depends purpose discussed kolmogorov complexity mailing list years ago httpmailmantieduchpipermailkolmogorov2004q1000112html one show smaller computable distance measure satisfying krafts inequality always right similarity relation right meaning clusters wrt desired property eg minimizing erroneous classifications using might sufficient perform desired classification task although universal mathematical sense imply universally good every clustering classification task hand experimental success universal metric remarkable light considerations even astonishing informationtheoretic dominant feature seems often one interested consideration may also explain variants sometimes fail would great one could augment adapt classification problem hand theoretically elegant way allow grouping wrt desired property best marcus ______________________ marcus hutter assoc prof rsise room b259 building 115 australian national university corner north daley road canberra act 0200 australia phone 6102 612 51605 fax 6102 612 58651 email jdykjnglapjsanueduau httprsiseanueduaumarcus machine learning summer school 2008 httpkioloa08mlsscc uai mailing list uai mailing list fri 08 aug 2008 022623 0800 uai define similarity