Under what conditions does fitcsvm in Matlab fail to return a decision hyperplane?
Overlapping datasets can cause headaches for identifying decision boundaries. I'm trying to understand when the fitcsvm can find a boundary and when it cannot. If I run the following two pieces of code, each on 10 measurements of a single observable, the difference is just in the membership of a single measurement.
f = [-1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
and
f = [-1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
When you calculate the decision boundary after running the first piece of code it returns a decision boundary of t=6.5 which seems fair. For the second piece of code SVMModel.Alpha.*SVMModel.SupportVectorLabels.*SVMModel.SupportVectors evaluates to 0 and the Bias is -1 which is indicative of no result (the boundary satisfies 0*t-1=0).
Can anyone tell me the conditions under which fitcsvm does and does not return a decision boundary. Help!
Thanks
matlab machine-learning svm
asked Nov 21 '18 at 15:42
Steve
11
add a comment |
Overlapping datasets can cause headaches for identifying decision boundaries. I'm trying to understand when the fitcsvm can find a boundary and when it cannot. If I run the following two pieces of code, each on 10 measurements of a single observable, the difference is just in the membership of a single measurement.
f = [-1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
and
f = [-1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
When you calculate the decision boundary after running the first piece of code it returns a decision boundary of t=6.5 which seems fair. For the second piece of code SVMModel.Alpha.*SVMModel.SupportVectorLabels.*SVMModel.SupportVectors evaluates to 0 and the Bias is -1 which is indicative of no result (the boundary satisfies 0*t-1=0).
Can anyone tell me the conditions under which fitcsvm does and does not return a decision boundary. Help!
Thanks
matlab machine-learning svm
asked Nov 21 '18 at 15:42
Steve
11
add a comment |
Overlapping datasets can cause headaches for identifying decision boundaries. I'm trying to understand when the fitcsvm can find a boundary and when it cannot. If I run the following two pieces of code, each on 10 measurements of a single observable, the difference is just in the membership of a single measurement.
f = [-1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
and
f = [-1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
When you calculate the decision boundary after running the first piece of code it returns a decision boundary of t=6.5 which seems fair. For the second piece of code SVMModel.Alpha.*SVMModel.SupportVectorLabels.*SVMModel.SupportVectors evaluates to 0 and the Bias is -1 which is indicative of no result (the boundary satisfies 0*t-1=0).
Can anyone tell me the conditions under which fitcsvm does and does not return a decision boundary. Help!
Thanks
matlab machine-learning svm
asked Nov 21 '18 at 15:42
Steve
11
Overlapping datasets can cause headaches for identifying decision boundaries. I'm trying to understand when the fitcsvm can find a boundary and when it cannot. If I run the following two pieces of code, each on 10 measurements of a single observable, the difference is just in the membership of a single measurement.
f = [-1 -1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
and
f = [-1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]';
t = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]';
SVMModel = fitcsvm(t, f);
When you calculate the decision boundary after running the first piece of code it returns a decision boundary of t=6.5 which seems fair. For the second piece of code SVMModel.Alpha.*SVMModel.SupportVectorLabels.*SVMModel.SupportVectors evaluates to 0 and the Bias is -1 which is indicative of no result (the boundary satisfies 0*t-1=0).
Can anyone tell me the conditions under which fitcsvm does and does not return a decision boundary. Help!
Thanks
matlab machine-learning svm
matlab machine-learning svm
asked Nov 21 '18 at 15:42
Steve
11
asked Nov 21 '18 at 15:42
Steve
11
asked Nov 21 '18 at 15:42
Steve
11
asked Nov 21 '18 at 15:42
Steve
11
asked Nov 21 '18 at 15:42
Steve
11
11
add a comment |
add a comment |
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