Breaking News: Grepper is joining You.com. Read the official announcement!

svm

Innocent Iguana answered on January 9, 2024 Popularity 9/10 Helpfulness 2/10

answer svm

svm

Comment

- Not all datapoints are linearly separable on lower dimension
- Transform such dataset to a higher dimensional space where it can be linearly separable by a hyperplane
- Support vectors: 
    - examples/data points closest to the hyperplane
    - both classified and misclassified datapoints are counted
    - If a datapoint is not a support vector, removing it will not affect the model
    - Small number of support vectors = fast kernel SVMs
- margin : distance from a support vector to decision boundary
- best decision boundary has equal distance from all support vectors
- The best separable line is the hyperplane has the biggest margin
- Measure of closeness : Regularization parameters (hinge loss and l2)
- Boundary decision lines : The lines that touches the support vectors / closest the the support vectors
- Soft margin SVM : Used when the classes are not separable (Controlled by regularization parameter)
- kernel : Sometimes it is difficult to caculate the mapping of transformation. So we use a shortcut called kernel that is computationally less expensive.
    - RBF : support vector = Difference between 2 inputs: X and X` 
    - C hyperparameter = regularization
    - gamma hyperparameter = smoothness of the boundary
- Stochastic gradient descent (SGD) : 
    - Similar to SVM, but scales well for large dataset
    - how : uses gradient descent to find out the maximised margin among possible margins.

Popularity 9/10 Helpfulness 2/10 Language whatever

Source: Grepper

Tags: svm whatever

Link to this answer
Share Copy Link

Contributed on Jan 09 2024

Innocent Iguana

0 Answers Avg Quality 2/10

svm

Contents

More Related Answers

svm

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.