These instances, whenmapped to an N-dimensional space, represent a core set that can be
used to construct an approximation to theminimumenclosing ball. Solving the SVMlearning
problem on these core sets can produce a good approximation solution in very fast speed.
For example, the core-vector machine [81] thus produced can learn an SVM for millions of
data in seconds.
標(biāo)簽：
N-dimensional
whenmapped
instances
represent
上傳時(shí)間：
2016-11-23
上傳用戶：lixinxiang
