michael hutchinson ylwyrpjpsfrmanuxconz original message dont want allow english locale work want international clients non russian denied email service ok_locales en ja ko th zh allow anything cyrillic char sets please note en mean english locale despite name applies western charsets including german umlauts swedisch french turkish etc basically everything uses characters post plus language specific chars ok talking turkey thanks providing much needed clarity ok_locales may employ technique yet pending whether get russian spam gates sorry mean troll kind offense apologies friday afternoon pretty sick work shouldnt taken list sorry however missed point getting detailed res good thing sure question properly handle charset encoding see subject example encoding matched rule point rule discussed aims something unfortunately charset encoding slightly complex definitely requires closing part regular expression found check_for_faraway_charset_in_headers headerevalpm hdr g hence reinventing wheel analogy wheels quite flexible also rule applies subject whereas ok_locales check mime parts trigger russian spam western subject question one written subject separate rule written raw line well score spam leave filing mua unless score 25 reached sa bins scoring subject lines makes ok sense used simply koi8r subject would score high enough filtered blocked im trying employ ive learned sa webpage writing multiple lowscoring rules instead bigscoring ones see flawed also admit working rather well moment mind taken time translate russian spam work spammy phrases quote phrases scored sa hope clarifies previous posts appreciated posts appreciated sorry mean comment cheers mike dlnirllccswaikatoacnz sat apr 26 155014 2008 message 6 date wed 27 feb 2008 075657 1300 nzdt peter reutemann subject wekalist explorer test options weka machine learning workbench list messageid 9665853419827220966585341fusraiajwebmailscmswaikatoacnz contenttype textplaincharsetiso88591 id like understand deeply crossvalidation work suppose kfold crossvalidation works way 1 training model k1 folds 2 testing model 1 fold see httpenwikipediaorgwikicrossvalidationkfold_crossvalidation 3 final model combination resulting k trained models theres final model generated evaluation results k traintest split pairs get averaged cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete ph 64 7 8585174 dlnirllcwaikatoacnz sat apr 26 155014 2008 message 7 date wed 27 feb 2008 083213 1300 peter reutemann subject wekalist attribute selection test set weka machine learning workbench list messageid 47c4693d4090702waikatoacnz contenttype textplain charsetiso88591 formatflowed since attribute selection pulls away attributes training set way store list attribute discarted remove attributes test set based reading stored attribute list youre performing classification might wan na look using metaclassifier filteredclassifier combination attributeselection filter could use attributeselected metaclassifier metaclassifiers adapt test sets store filterattribute selection setup used training reuse test set use attribute selection manually via wekaattributeselectionattributeselection class could pass datasets reducedimensionalityinstances method order remove attributes see javadoc information maybe operation takes time attribute selection removing attributes doesnt take long hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 vafzspentahoorg sat apr 26 155014 2008 message 8 date wed 27 feb 2008 093600 1300 mark hall subject wekalist select attributes cross validation cmxlleedxbbptsuperevait weka machine learning workbench list messageid 20080226203557ga20245cygnuslocal contenttype textplain charsetusascii need use wrappersubseteval implements wrapper approach subset selection ie repeated 5fold cross validation training data respect classifier order measure goodness given subset crossvalidation option attribute selection mode part tab global evaluation option selected entire attribute selection process repeated x folds data eg would x separate runs wrappersubsetevalgreedystepwise resulting separate probably different set attributes output attribute data many folds resulted attribute selected part final subset ie gives indication stability feature selection process cheers mark tue feb 26 2008 060623pm 0100 thomas wrote understand whats meaning crossvalidation option select attributes tab explorer want backward elimination greedystepwise evaluated classifier classifiersubseteval ie want try eliminating one attribute per time perform crossvalidation accuracy better permanently eliminate attribute proceed another elimination classifiersubseteval editor choose beetwen holding test set using training set crossvalidation option classifier editor directly selct attributes tab dont understand meaning crossvalidation evaluated example reliefattributeeval doesnt sense classifier evaluation external option editor purpose ive axplained check crossvalidation option choose use training classifiersubseteval thanx thomas dadanet fai nuove amicizie e guadagna con loro grazie 1friend references visible links 1 httpfriendsdadanetfriendspeoplephp _______________________________________________ wekalist mailing list iuxxdkqklistscmswaikatoacnz httpslistscmswaikatoacnzmailmanlistinfowekalist mark hall senior developerconsultant pentaho open source business intelligence citadel international suite 340 5950 hazeltine national dr orlando fl 32822 usa 64 7 8473537 office 64 21 399132 mobile 1 815 5508637 fax skype markandrewhall yahoo mark_andrew_hall download latest release today _______________________________________________ wekalist mailing list iuxxdkqklistscmswaikatoacnz httpslistscmswaikatoacnzmailmanlistinfowekalist end wekalist digest vol 60 issue 36 thu 07 aug 2008 045512 1300 fw rule russian character sets koi8r quite acharset