Survey
* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
Download Lecture 19
Transcript
STRENGTHS AND WEAKNESSES OF SVM ï Strengths ï Training is relatively easy ï No local optimal, unlike in neural networks It scales relatively well to high dimensional data ï Tradeoff between classifier complexity and error can be controlled explicitly ï Non-traditional data like strings and trees can be used as input to SVM, instead of feature vectors ï Inherent feature selection capability ï ï Weaknesses ï Need to choose a âgoodâ kernel function.
