active coevolutionary learning of deterministic finite automata - 2005new

active coevolutionary learning of deterministic finite automata - 2005new

ID:34400447

大小:232.66 KB

页数:28页

时间:2019-03-05

active coevolutionary learning of deterministic finite automata - 2005new_第1页
active coevolutionary learning of deterministic finite automata - 2005new_第2页
active coevolutionary learning of deterministic finite automata - 2005new_第3页
active coevolutionary learning of deterministic finite automata - 2005new_第4页
active coevolutionary learning of deterministic finite automata - 2005new_第5页
资源描述:

《active coevolutionary learning of deterministic finite automata - 2005new》由会员上传分享,免费在线阅读,更多相关内容在教育资源-天天文库

1、JournalofMachineLearningResearch6(2005)1651–1678Submitted8/04;Revised6/05;Published10/05ActiveCoevolutionaryLearningofDeterministicFiniteAutomataJoshBongardJOSH.BONGARD@CORNELL.EDUHodLipsonHOD.LIPSON@CORNELL.EDUComputationalSynthesisLaboratorySibleySchoolofMechanicalandAerospaceEngineeringI

2、thaca,NY14853,USAEditor:StefanWrobelAbstractThispaperdescribesanactivelearningapproachtotheproblemofgrammaticalinference,specif-icallytheinferenceofdeterministicfiniteautomata(DFAs).Werefertothealgorithmastheestimation-explorationalgorithm(EEA).Thisapproachdiffersfrompreviouspassiveandactive

3、learningapproachestogrammaticalinferenceinthattrainingdataisactivelyproposedbytheal-gorithm,ratherthanpassivelyreceivingtrainingdatafromsomeexternalteacher.Hereweshowthatthisalgorithmoutperformsoneversionofthemostpowerfulsetofalgorithmsforgrammaticalinference,evidencedrivenstatemerging(EDSM

4、),onrandomly-generatedDFAs.TheperformanceincreaseisduetothefactthattheEDSMalgorithmonlyworkswellforDFAswithspecificbal-ances(percentageofpositivelabelings),whiletheEEAismoreconsistentoverawiderrangeofbalances.BasedonthisfindingweproposeamoregeneralmethodforgeneratingDFAstobeusedinthedevelopme

5、ntoffuturegrammaticalinferencealgorithms.Keywords:grammaticalinference,evolutionarycomputation,deterministicfiniteautomata,activelearning,systemidentification1.IntroductionGrammaticalinferenceisapopularmachinelearningdomain(refertoCicchelloandKremer,2003,foranoverview):ithaswideapplicabilityi

6、nbothcomputationallinguisticsandrelatedfields,aswellasgivingrisetoahostofbenchmarkproblems(Tomita,1982;Langetal.,1998)andcompe-titions.Grammaticalinferenceisaspecialcaseofthelargerproblemdomainofinductivelearning(BergadanoandGunetti,1995),whichaimstoconstructmodelsofsomeunderlyingsystembased

7、onsetsofpositiveandnegativeclassifications.Inoneclassofgrammaticalinferencemethods,thesystemisconsideredtobesomekindoflanguageorclassifier,andmodelsarerepresentedasdeterministicfiniteautomata(DFA).Boththetargetsystemandmodelstakestringsofsymbolsasinput(sent

当前文档最多预览五页,下载文档查看全文

此文档下载收益归作者所有

当前文档最多预览五页,下载文档查看全文
温馨提示:
1. 部分包含数学公式或PPT动画的文件,查看预览时可能会显示错乱或异常,文件下载后无此问题,请放心下载。
2. 本文档由用户上传,版权归属用户,天天文库负责整理代发布。如果您对本文档版权有争议请及时联系客服。
3. 下载前请仔细阅读文档内容,确认文档内容符合您的需求后进行下载,若出现内容与标题不符可向本站投诉处理。
4. 下载文档时可能由于网络波动等原因无法下载或下载错误,付费完成后未能成功下载的用户请联系客服处理。