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In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C

In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C Correct Answer we can still classify the data correctly for given setting of hyper parameter c

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Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very large value of C(C->infinity)?
User level threads are threads that are visible to the programmer and are unknown to the kernel. The operating system kernel supports and manages kernel level threads. Three different types of models relate user and kernel level threads. Which of the following statements is/are true ? (a) (i) The Many - to - one model maps many user threads to one kernel thread (ii) The one - to - one model maps one user thread to one kernel thread (iii) The many - to - many model maps many user threads to smaller or equal kernel threads (b) (i) Many - to - one model maps many kernel threads to one user thread (ii) One - to - one model maps one kernel thread to one user thread (iii) Many - to- many model maps many kernel threads to smaller or equal user threads
Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very small C (C~0)?
Let D be a simple graph on 10 vertices such that there is a vertex of degree 1, a vertex of degree 2, a vertex of degree 3, a vertex of degree 4, a vertex of degree 5, a vertex of degree 6, a vertex of degree 7, a vertex of degree 8 and a vertex of degree 9. What can be the degree of the last vertex?
To build a modularized kernel, the kernel-headers and kernel-source packages must first be installed. Where will you find the kernel-headers and kernel-source?
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We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
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3. Feature normalization always helps when we use Gaussian kernel in SVM
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A quadratic polynomial in x leaves remainder 4, 4, and 0 respectively. when divided by (x - 1), (x - 2) and x - 3 Find the quadratic polynomial: