文件名称:lec1
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The p erceptro n algo rithm ceases to update the para meters only when all the training
images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are
p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such
a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of
updates (mistakes ). We’ll sho w this in lectur e 2.-The p erceptro n algo rithm ceases to update the para meters only when all the training
images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are
p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such
a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of
updates (mistakes ). We’ll sho w this in lectur e 2.
images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are
p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such
a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of
updates (mistakes ). We’ll sho w this in lectur e 2.-The p erceptro n algo rithm ceases to update the para meters only when all the training
images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are
p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such
a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of
updates (mistakes ). We’ll sho w this in lectur e 2.
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