wblciakzcsorstedu pattern recognition contest prc sender wblciakzmaillistcsorstedu precedence bulk pattern recognition long continues major component machine intelligence decision analysis uncertainty years large number methods algorithms developed ranging perceptrons clustering algorithms bayesian neurofuzzy classifiers svm art som classifiers large literature addresses benchmarking performance evaluation backdrop suggested following conceptually simple scheme may contribute better understanding capabilities various approaches scheme format contest referred pattern recognition contest prc short suggestion tentative rules defined precisely furthermore contrast conventional methods evaluation prc leads rankings fuzzy rather crisp fuzziness rankings reflection fact realistic settings evaluation performance lend conclusions precise definitive stated briefly point departure prc assumption n1 entrants eo ei en eo representing human h serves point reference ei i1 n representing algorithm example e1 may svm classifier special version classifier communication ei coaching judge manager chose black red pattern unit square concreteness pattern may 4x4 checkboard ei know pattern chose point random present ei ask ei tell whether point black red ei guesses color correctly ei gets one dollar ei pays one dollar end first step ei knows correct answer since test sequence consists one point ei better toss coin come answer chose another point random repeat procedure process continues n number points test sequence increases ei improve performance increase earnings earnings tabulated value n human entrant h allowed see emerging pattern black red points step step n total earnings tabulated entrant rei n representing total earnings ei earnings smoothed use moving average resulting smoothed earnings sequence sh 1 sh 2 sh n likewise ei i1 n speed evaluation tests may batched results tabulated end batch process terminates time entrants frequency correct answers approaches unity point n1 total earnings sh se1 sen results contest determined analysis pattern smoothed earning sequences beginning end contest among attributes sequence two primary importance total earnings end test b length test sequence end frequency correct answers close unity attribute may interpreted indicator speed learning simplify evaluation results total earnings scale may granulated granule labeled fuzzy set eg low medium high high etc granulation consistent imprecision definition termination analyzing earning sequences may easier employ pairwise comparisons case ahp analytic hierarchy process techniques may used arrive granulated linear ordering preordering see yamashita yonezawa effective use fuzzy ahp supporting human decision making journal japan society fuzzy theory intelligent informatics 161 2632 2004 case regular pattern like checkboard likely human entrant would hard beat heshe see pattern points step tougher test would irregular pattern distorted regular pattern conclusion underscored format contest suggested foregoing tentative informal basic idea may point right direction results preliminary testing may obtained dr masoud nikravesh httpwwwbisccsberkeleyprc tue 05 aug 2008 172226 0700 nan