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Q. |
## The kernel trick |

A. | can be applied to every classification algorithm |

B. | is commonly used for dimensionality reduction |

C. | changes ridge regression so we solve a d ?? d linear system instead of an n ?? n system, given n sample points with d features |

D. | exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points |

Answer» D. exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points |

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- Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1.
- What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It�s a similarity function
- We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1.We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables 3. Feature normalization always helps when we use Gaussian kernel in SVM
- We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
- We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
- What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
- We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM