Still effective in cases where number of dimensions is greater
than the number of samples.
Tags
#SVM
Question
[default - edit me]
Answer
?
Tags
#SVM
Question
[default - edit me]
Answer
Still effective in cases where number of dimensions is greater
than the number of samples.
If you want to change selection, open document below and click on "Move attachment"
1.4. Support Vector Machines — scikit-learn 0.23.2 documentation (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high dimensional spaces. <span>Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: different Kernel functions can be specified for the decisi
Summary
status
not learned
measured difficulty
37% [default]
last interval [days]
repetition number in this series
0
memorised on
scheduled repetition
scheduled repetition interval
last repetition or drill
Details
No repetitions
Discussion
Do you want to join discussion? Click here to log in or create user.