Agnostic learning of geometric patterns (extended abstract)
Sally A. Goldman, Stephen S. Kwek, Stephen Scott · 1997
Goldberg,Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class.We present an on-line agnostic learning algorithm for learning the class of one-dimensional geometric patterns.Since, when moving from the processed visual image to a one-dimensional pattern some key information is lost, we define a class of two-dimensional geometric patterns for which the important features from the visual image are incorporated in the geometric pattern, and show how to extend our agnostic learning algorithm to this class of two-dimensional patterns.Prnllissiofl lo InnLc digltnl!l~nrd copies ofall or p;m ofllli\ tll;~ir'r~i~l lb, penonnl or cl~wsroom wsc is ~I-mli'd w~tlwut I>c prob &d th;~r 111~ cop<\ arc IMM made or distribukd IOr profit or commcrc~~~l .~tl\x~lt;~gc.tile copyrlglit nolice.Ihe liilc oflhc pul~lic;llio~l and iLv J;kIc ;nppc;kr.:III~ llollcc jl: giwn that copyrighl is hy pmn~~on ofthe ,\c'kl.IIK '1'0 copy OLIIC~W~?.~'. 10 rrpuhlirh.10 posl 01) wvm or lo rtxilstrihulc IO 11~15.rqutrcs hpcClliC pemlission and/or Ibe ( 'OL.7' 97 Naslnille.Tcnncsec.USA Copyright I997 i\Ckl O-R9791 -89 I -(v97'7..$? 50