H13-311_V3.5 · Question #20
SVM What are the commonly used kernel functions in?
The correct answer is A. Gaussian kernel function B. Polynomial kernel function C. Sigmiod Kernel function D. Linear kernel. All four options are correct because SVMs support multiple kernel functions to handle different data distributions and problem types. The Gaussian (RBF) kernel maps data into infinite-dimensional space and works well for non-linear problems with no prior knowledge of the data…
Question
SVM What are the commonly used kernel functions in?
Options
- AGaussian kernel function
- BPolynomial kernel function
- CSigmiod Kernel function
- DLinear kernel
How the community answered
(31 responses)- A100% (31)
Explanation
All four options are correct because SVMs support multiple kernel functions to handle different data distributions and problem types. The Gaussian (RBF) kernel maps data into infinite-dimensional space and works well for non-linear problems with no prior knowledge of the data. The Polynomial kernel captures feature interactions up to a specified degree, the Sigmoid kernel mimics neural network activation and is used in certain classification tasks, and the Linear kernel is the simplest form, effectively making the SVM a linear classifier suitable for linearly separable data.
Since all choices are correct, there are no wrong distractors - this question tests whether you know that SVM is not limited to a single kernel but instead offers a family of kernel functions adaptable to different scenarios.
Memory tip: Use the acronym "GPSL" (Gaussian, Polynomial, Sigmoid, Linear) to recall the four standard SVM kernels, ordered roughly from most to least commonly used in practice.
Topics
Community Discussion
No community discussion yet for this question.