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cohen-icml95 - T oappearin/M ach ineL earn ing:P roceed...

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Unformatted text preview: T oappearin/M ach ineL earn ing :P roceed ingso ftheTw e lfthIn ternat iona lC on ference"(M L) F astEect iveR u leInduct ion W illiamW .C ohen AT&TB e llLaborator ies M ounta inA venueM u rrayH ill,N J [email protected] A b stract M anyex ist ingru lelearn ingsy stem sare com pu tat iona llyexpen s iveonlargeno isy datasets . Inth ispaperw eeva luatethe recen t ly-p roposedru lelearn inga lgor ithm IREPonalargeandd iverseco llect iono f benchm arkp rob lem s .W eshow thatwh ile IREP isex trem e lyec ient ,itfrequen t ly g iveserrorratesh igherthanthoseo fC . andC .ru les . W ethenp roposeanum- bero fm od icat ion sresu lt inginana lgo- r ithmR IPPER k thatisverycom pet it ive w ithC .ru lesw ithrespecttoerrorrates , bu tm uchm oreec ientonlargesam p les . R IPPER k ob ta in serrorrateslow erthanor equ iva len ttoC .ru leson o f bench- m arkp rob lem s ,sca lesnear lylinear lyw ith thenum bero ftra in ingexam p les ,andcan ec ient lyp rocessno isydatasetscon ta in ing hund red so fthou sand so fexam p les . INTRODUCT ION Sy stem sthatlearnsetso fru leshaveanum bero fde- s irab lep ropert ies . R u lesetsarere lat ive lyeasyfor peop letounderstand [C at lett , ],andru lelearn- ingsy stem sou tperform dec is iontreelearnersonm any p rob lem s[P aga lloandH au ss ler , ;Q u in lan , ; W e issandIndu rkhya , ].R u lesetshaveanat- u ra landfam iliarrstordervers ion ,nam e lyP ro log p red icates ,andtechn iquesforlearn ingp ropos it iona l ru lesetscano ftenbeex tendedtotherst-ordercase [Q u in lan , ; Q u in lanandC am eron-Jones , ]. C erta intypeso fp r iorknow ledgecana lsobeeas ily comm un icatedtoru lelearn ingsy stem s[C ohen , ; P azzan iandK ib ler , ]. O new eaknessw ithru lelearn ingsy stem sisthatthey o ftensca lere lat ive lypoor lyw iththesam p les ize ,par- t icu lar lyonno isydata [C ohen , ].G iventhep reva- lenceo flargeno isydatasetsinrea l-w or ldapp licat ion s , th isp rob lem iso fcr it ica lim portance .Thegoa lo fth is paperistodeve lopp ropos it iona lru lelearn inga lgo- r ithm sthatperform ec ient lyon largeno isydatasets , thatex tendnatu ra llytorst-orderrep resen tat ion s , andthatarecom pet it iveingenera lizat ionperform ance w ithm orem atu resym bo liclearn ingm ethod s ,such asdec is iontrees .Theendp roducto fth iseortis thea lgor ithmR IPPER k ,wh ich iscom pet it ivew ith C .ru lesw ithrespecttoerrorrates ,sca lesnear ly lin- ear lyw iththenum bero ftra in ingexam p les ,andcan ec ient lyp rocessno isydatasetscon ta in inghund red s o fthou sand so fexam p les . PREV IOU SW ORK . COM PLEX ITYOFRULEPRUN ING M anyo fthetechn iquesu sed inm odernru lelearn- ershavebeenadap tedfromdec is iontreelearn ing . M ostw ide ly-u seddec is iontreelearn ingsy stem su sean overt-and-sim p lify learn ingstrategytohand leno isy data :ahypothes isisform edbyrstgrow ingacom- p lextreewh ich/overts"thedata ,andthens im- p lify ingor prun ing thecom p lextree[Q u in lan , ; M ingers , ].U sua lly(bu tnota lw ay s)suchp run ing...
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