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8 The Importance of Ontological Structure: Why Validation by ‘Fit-to-Data’...  163


                                                                  class),  (continued)

                                             p)                   each  j,1,0)
                                             D                    for
                                             p                    used  DD


                               model)  “maximum”)  model)  “euclidean”)  model)  “minkowski”,  model)  “maximum”) sqrt(sum((vdata--model)ˆ2)/length(vdata))  being  number  one  ifelse(model[i]

                           sum(abs(vdata--model))  rbind(vdata,  D method sqrt(sum((vdata--model)ˆ2))  rbind(vdata,  D method  rbind(vdata,  D method  max(abs(vdata--model))  rbind(vdata,  D method sum((vdata--model)ˆ2)  with  classes,    model[i]<









                      code       <  dist(dm,    <  dist(dm,    <  dist(dm,    <  dist(dm,  (i.e.
                      R      or  dm  or  dm  dm  a  or  dm        variables  j,1,0)and

                           vector  vectors.  differences  implies  Zero  norm.  the  for  nominal  DD

                           model   model     Zero  values.  L 2  the  for
                           and     and     the  of  of  pair  of  corrected  used  ifelse(vdata[i]
                           data    data    power  elements.  any  square  norm  fit  be  also
                           the  fit  the    pth  vector    the  L 2  perfect  could
                           between  perfect  a  between  fit  the  of  model  between  simply  fit  the  is  a  implies  case  vdata[i]<


                           distance  implies  Zero  distance  perfect  a  sum  the  of  and  data  difference  fit  perfect  is  error  perfect  a  error  Zero  data.  This variable)  j:  class


                      Description  variable)  absolute  Total  elements.  Euclidean  The  implies  Zero  root  pth  The  the  between  fit  perfect  maximum  The  a  implies  squared  of  Sum  implies  Zero  Root-mean-squared  the  of  size  (Boolean  each  for


                   validation  (cardinal                          2f0, 1g  Here,



                   of   2R                                (SSE)  error  class.
                   Measures  model[i]                     squared  model[i]  each  for



                   8.3     norm    norm           norm    of  error  vdata[i],  repeated
                   Table  Metric  vdata[i],  L 1  L 2  norm  p  L 1  Sum  RMS  if
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