dave plater bicorjinkyyahoocouk per jessen wrote carlos e r wrote think worth noting somewhat highend system like still uses barely 250w _full_ load still dont know new measurement gadgets trusted mean dont know bad dont know trusted cant really think reason guess could find welldefined load large wattage resistor instance check important thing power needed internal dc lines one said mb nowdays uses power 12 v line also video card uses dont know plus fans high speed drives sum close 10 amps 350w unit supply 10 instance really need higher wattage power supply need much power one lines uses much power powersupply higher output rating 12v line youre right point taken ie peak power spin 28 amps 12v line 08a avg maximum seeks power supply must able handle true mb uses also line problems well although measurement device measure peaks harddisk spinup time 345 seconds think would seen excess load startup machine used 175w per jessen zürich going temperature info machine bombs due temperature spike cores working hard nothing wrong psu dave cmxlleedxbbptsuperevait sat apr 26 154927 2008 message 4 date tue 26 feb 2008 180623 0100 cet thomas subject wekalist select attributes cross validation iuxxdkqklistscmswaikatoacnz messageid 2008022617062315bc9180974feva125dadanetit contenttype textplain charsetiso88591 html attachment scrubbed url httpslistscmswaikatoacnzpipermailwekalistattachments200802266bbdd63aattachment0001html cmxlleedxbbptsuperevait sat apr 26 154927 2008 message 5 date tue 26 feb 2008 182743 0100 cet thomas subject wekalist racesearch startset iuxxdkqklistscmswaikatoacnz messageid 20080226172743f1ff718077a0eva124dadanetit contenttype textplain charsetiso88591 html attachment scrubbed url httpslistscmswaikatoacnzpipermailwekalistattachments20080226a43f0854attachment0001html dlnirllccswaikatoacnz sat apr 26 154927 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 154927 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 154927 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 wed 06 aug 2008 005556 0200 opensuse ot much power pc really consume