文件名称:lec3
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The Support Ve ct or Machi ne
So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has
a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the
training images (e xamples). Such a larg e margin classifier seems like one we would like to
use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support
Ve ctor M achine or SVM for short. -The Support Ve ct or Machi ne
So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has
a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the
training images (e xamples). Such a larg e margin classifier seems like one we would like to
use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support
Ve ctor M achine or SVM for short.
So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has
a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the
training images (e xamples). Such a larg e margin classifier seems like one we would like to
use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support
Ve ctor M achine or SVM for short. -The Support Ve ct or Machi ne
So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has
a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the
training images (e xamples). Such a larg e margin classifier seems like one we would like to
use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support
Ve ctor M achine or SVM for short.
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