text_combined: searchstoragecom vbadpnbtgumjqliststechtargetcom week february 11 2008 published searchstoragecom week issue products year 2007 winners special report highperformance nas itknowledge exchange backing exchange servers products year 2007 winners searchstoragecom storage magazine editors theres lesson learned 2007 storage magazine searchstoragecom products year vendors cant rest laurels new guys block pop every day fresh ideas technologies better ways things vendors last years winners list back none 2006 gold medal winners showed year youll keep reading find winners 2007 products year winners special report highperformance nas searchstoragecom editors traditionally nas systems limitations throughput connectivity reliability scalability result missioncritical storage tasks still required san today new generation highperformance nas systems promises overcome past limitations bringing santype features capabilities filebased storage highperformance nas itknowledge exchange backing exchange servers searchstoragecom reader wants know best approach backing exchange server respond question start discussion visit itknowledge exchange podcasts webcasts benefits networked storage virtual servers available demand speaker mark peters analyst enterprise strategy group sponsor regan technologies using cdp data distribution migration available demand speaker jerome wendt president lead analyst dcig inc sponsor ca building highly virtualized storage environment available demand speaker steve duplessie founder senior analyst enterprise strategy group sponsor arrow electronicscolorado best practices infrastructure alignment remote office available demand speakers mike perkowski chief operating officer cofounder microcast communications mathew dickson vice president development product line manager recovery management ca sponsor ca getting handle iscsi available demand speaker mark bowker analyst enterprise strategy group sponsor equallogic view podcasts webcasts site 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libsvm weka machine learning workbench list messageid 47a768be4070004waikatoacnz contenttype textplain charset3diso88591 format3dflowed sily importante question oneclass learning using libsvm training set one label testing libsvm oneclass test set one label well wondering thats right way testing since stupid classifier says example classlabel would solve problem im guessing use maybe test set two labels oneclass classification normally used determining outliers way ive fit oneclass svm weka returning missing instance considered outlier otherwise classifiying data one dataset wouldnt make much sense cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 dlnirllcwaikatoacnz sun feb 24 102732 2008 message 3 date tue 05 feb 2008 084913 1300 peter reutemann subject wekalist problem running java large arff file weka machine learning workbench list messageid 47a76c394020706waikatoacnz contenttype textplain charset3diso88591 format3dflowed stuck problem bellow trying run classifier like smo ibk weka348a 64bit opteron system 2gb ram running os rocks 42 input arff file 2gb size running via command prompt ending following error exception thread main javalangoutofmemoryerror java heap space usually program ran fine smaller arff files bellow java xss768k xmx1024m wekaclassifiersfunctionssmo time increased memory size bellow java verbosegc xms2048m xmx2048m wekaclassifiersfunctionssmo ending following error full gc 1941813k1941813k2019520k 01178400 secs full gcunloading class sunreflectgeneratedserializationconstructoraccessor2 unloading class sunreflectgeneratedserializationconstructoraccessor1 1941813k1933707k2019520k 387892380 secs full gc 1941823k1941823k2019520k 110075420 secs full gc 1941823k1941809k2019520k 01388310 secs full gc 1941887k1941887k2019520k 01078960 secs full gc 1941887k1941879k2019520k 01080050 secs exception thread main javalangoutofmemoryerror java heap space full gc 1941887k148k2019520k 00438670 secs anybody help running classifiers big arff file 24gb size schemes weka data memory means provide enough heap space depending scheme internal preprocessing performs additional space necessary eg applying normalize standardize filter data schemes werent designed work large datasets might also wan na try incremental scheme capable loading data instance instance instead everything ie classifier implements wekaclassifiersupdateableclassifier interface check javadoc interface see classifiers implement hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 dlnirllcwaikatoacnz sun feb 24 102732 2008 message 4 date tue 05 feb 2008 085518 1300 peter reutemann subject wekalist random forest visualization weka machine learning workbench list messageid 47a76da68000802waikatoacnz contenttype textplain charset3diso88591 format3dflowed way get information trees generated random forest algorithm default modify code randomforest uses wekaclassifiersmetabagging internally bagging contains array m_classifiers type wekaclassifiersclassifier contains single trees array output tostring method modify tostring method randomforest class output built bagging classifier well ie add m_baggertostring call returned string hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 zpironumbcedu sun feb 24 102732 2008 message 5 date mon 4 feb 2008 163026 0500 est dongsong zhang subject wekalist question prediction command line iuxxdkqklistscmswaikatoacnz messageid contenttype textplain charset3dusascii format3dflowed generated following predictions using p option command line difficulty understanding values predictions example two types predictions instances w ith value 066 assigned class others n applies instances 099 prediction could explain inconsistency whats wrong 0 06649484536082474 1 09948453608247423 2 06649484536082474 n 3 n 06649484536082474 n 4 06649484536082474 5 06649484536082474 6 n 06649484536082474 n 7 n 06649484536082474 n 8 06649484536082474 9 06649484536082474 10 n 09948453608247423 n 11 n 06649484536082474 n 12 09948453608247423 13 09948453608247423 14 n 06649484536082474 n 15 n 09948453608247423 n 16 n 09948453608247423 17 06649484536082474 18 n 06649484536082474 n 19 n 06649484536082474 n 20 09948453608247423 21 06649484536082474 22 09948453608247423 n 23 n 09948453608247423 n 24 09948453608247423 25 06649484536082474 26 09948453608247423 n 27 09948453608247423 n 28 n 06649484536082474 29 06649484536082474 30 n 09948453608247423 n 31 06649484536082474 n thanks zhang dlnirllcwaikatoacnz sun feb 24 102732 2008 message 6 date tue 05 feb 2008 110808 1300 peter reutemann subject wekalist question prediction command line weka machine learning workbench list messageid 47a78cc87020400waikatoacnz contenttype textplain charset3diso88591 format3dflowed generated following predictions using p option command line difficulty understanding values predictions example two types predictions instances value 066 assigned class others n applies instances 099 prediction could explain inconsistency whats wrong 0 06649484536082474 1 09948453608247423 2 06649484536082474 n 3 n 06649484536082474 n 4 06649484536082474 5 06649484536082474 output explained weka primer httpwekasourceforgenetwekadocindexphpenprimer search p 0 cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 udndxebiomaporg sun feb 24 102732 2008 message 7 date mon 4 feb 2008 175045 0900 amandeep sidhu subject wekalist last cfp 3rd special track ontologies biomedicalsystems cbms 2008 thbpzbtqxeqsvcyahoogroupscom thbpzbtqxeqsvcumnedu pfsdddnckgenomestanfordedu elfohxlistsccsneuedu gxxvsvegvmuomvanbugorg awhhqcalfbpqwgfpawhhlistsoptimanumericscom thbpzbtqxeqsvcsdscedu nltkfmeqfoegimperialacuk kutwzghylistsopenbioorg nftvtlinetbionet gbetmdnetbionet vmogpvuylxlistsopenbioorg iafysqastkhnxinlistsopenbioorg ntuorlistsibiblioorg jygxvxxomantgetnjlistsbinfkudk thbpzbtqxeqsvclistnihgov bqagbeqsnkanqasisorg thbpzbtqxeqsvcjiscmailacuk zmkohdiwiqspncbinlmnihgov nbvhuccweizmannacil askfvcvalistsbioinformaticsunsweduau qaz_nmwovqhk_nkuyobioinformaticsorg kelcxqsoucuzzpjlkelclistsbioinformaticsunsweduau thbpzbtqxeqsvclistservdartmouthedu jpnilxpsfuaanrtpjpnilxpsfuaanrtpjpnilximperialacuk sb2hbpdmaillistunibasch ihelksjetfkqzpsbforumorg qbnvbssbcsuse vjtszcnumailontonetorg kwjafquqmpdzgbfmkwmailontonetorg ydqfgaihnwrefyenvgennoxacuk bldmnqotskqvlenvgennoxacuk itflistscmswaikatoacnz trcyzovszbmbioinformaticsorg esyxcebioinformaticsorg nbvhucccsuiucedu yxwfoermeanybiochemuthscsaedu uhvvontaovqxceyjuslugorgau mqdocbkavcclnet olldlhimbuqeduau hxkrqdwojnbioinformaticsorg nqmenvynetbionet oeadlistsmurdocheduau prtmlbldlistsderiorg gvtbuw22listsderiorg zmpvpzogforg gppkminpdlistsderiorg ctvmbowdwqiw3org kjvtbkwizdhflw3org vhxoyzjcrikmdrw3org olkgdkeunivieacat cakddnjyahoogroupscom gmtjuiebjztcvcw3org atjscienceuvanl tzfhcdgsimilemitedu ewdrlfitoabybiboewlistpandorabotscom humvbkqshbstybjlistalicebotorg brpsdlistsmindswaporg lthnafprsvelsnetorg aomopdesixsemanticplanetcom iuxxdkqklistscmswaikatoacnz messageid 5gcu36r94589659850c4363fo58z6dhg5gc4b63f4t89mailgmailcom contenttype textplain charset3diso88591 3rd special track ontologies biomedical systems 21st ieee international symposium computerbased medical systems jyve4skyle4 finland june 1719 2008 httpcbms08biomaporg call papers biomedical ontologies developed uncoordinated way often reflecting mere relations association called concepts serving primarily purposes information extraction online biomedical literature databases recent years learned great deal criteria must satisfied ontology allow true information integration automatic reasoning across data information derived different sources goal track survey existing biomedical ontologies reform way allow true information integration biomedical domain authors invited submit original papers exploring theories techniques applications biomedical ontologies papers invited limited following themes biomedical ontologies genetics proteomics diseases privacy etc conceptual models biological medical data semantics biological data modeling use semantics manage interoperation biomedical databases semantic web technologies formalisms biomedical data ontology representation exchange languages bioinformatics biomedical ontologies owl biological data integration management using ontologies biomedical data engineering using ontologies application biomedical ontologies heterogeneous database access query optimization techniques biomedical database using ontologies support ontologies biological information retrieval web services change management biomedical ontologies tools development management biomedical ontologies important dates 04 february 2008 paper submission due 28 february 2008 notification acceptance 28 march 2008 final cameraready paper due 28 march 2008 preregistration deadline paper submission procedures submitted papers original containing new original results use maximum 6 pages ieee twocolumn format including figures references submissions done electronically via cbms 2008 web submission system httpwwweasychairorgconferencesconf3dcbms200 8 submission implies willingness least one authors register present paper cbms 2008 symposium papers peer reviewed least two independent referees accepted papers included conference proceedings high quality submissions published special issue biomedical ontologies journal oxford university press track organizers amandeep sidhu curtin university technology perth httpwwwamandeeporg tharam dillon curtin university technology perth jake chen indiana university usa httpinformaticsiupuiedupeopleprofilephpid3d74 elizabeth chang curtin university technology australia httpwwwfitcbscurtineduauchangdocsindexphp program committee alexey tsymbal siemens germany david hansen ehealth research centre csiro australia david taniar monash university australia ernesto damiani computer science department milan university italy fabio porto database laboratory epfl switzerland farookh k hussain curtin university technology australia fedja hadzic university technology sydney australia huiru zheng university ulster uk jake chen indiana university usa james geller new jersey institute technology usa jason wang new jersey institute technology usa li liao university delaware usa ling feng tsinghua university china maja hadzic curtin university technology australia midori harris go consortium european bioinformatics institute mustafa jarrar vrije universiteit brussel belgium robert meersman vrije universiteit brussel belgium silke eckstein technical university braunschweig germany sourav bhowmick nanyang technological university singapore sun kim school informatics indiana university usa wenny rahayu la trobe university australia werner ceusters new york state center excellence bioinformatics life sciences usa yi pan georgia state university usa next part html attachment scrubbed url httpslistscmswaikatoacnzpipermailwekalistattachments2008020 444a92552attachmenthtml _______________________________________________ wekalist mailing list iuxxdkqklistscmswaikatoacnz httpslistscmswaikatoacnzmailmanlistinfowekalist end wekalist digest vol 60 issue 5 wed 06 aug 2008 014924 0000 week products year winners highperformance nas