the group of blue balls and red balls are separable by a straight line/linear line Let's consider a scenario like shown below X2 XI X2 X1 Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data? OSVM can not classify this data. OSVM give us error. The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and finds the best hyperplane that maximizes the margin. SVM is robust to outliers. OSVM is not robust to outliers.
the group of blue balls and red balls are separable by a straight line/linear line Let's consider a scenario like shown below X2 XI X2 X1 Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data? OSVM can not classify this data. OSVM give us error. The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and finds the best hyperplane that maximizes the margin. SVM is robust to outliers. OSVM is not robust to outliers.
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Transcribed Image Text:the group of blue balls and red balls are separable by a straight line/linear line
Let's consider a scenario like shown below
X2
O SVM can not classify this data.
X1
X2
Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data?
O SVM is not robust to outliers.
X1
O SVM give us error.
O The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and finds
the best hyperplane that maximizes the margin. SVM is robust to outliers.
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