Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

Related MCQs

• Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
• Multivariate split is where the partitioning of tuples is based on a combination of attributes rather than on a single attribute.
• 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
• 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.
• 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
• 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
• There are also many univariate methods that can be used in order to select the best features according to specific criteria based on              .
• There are also many univariate methods that can be used in order to select the best features according to specific criteria based on________.