For vectors covariance is defined as the dot product of two vectors. The value of covariance can vary from positive infinity to negative infinity. If the two distributions or vectors grow in the same direction the covariance is positive and vice versa. The Sign gives the direction of variation...
They support the classifier margins, so they are called Support Vectors. The distance between the classifier and the nearest points is called Marginal Distance. There can be several classifiers possible but we choose the one with the maximum marginal distance. So, the marginal distance and the ...
Mathematically, if we represent the distribution using vectors, correlation is said to be the cosine angle between the vectors. The value of correlation varies from +1 to -1. +1 is said to be a strong positive correlation and -1 is said to be a strong negative correlation. 0 implies no...
For vectors covariance is defined as the dot product of two vectors. The value of covariance can vary from positive infinity to negative infinity. If the two distributions or vectors grow in the same direction the covariance is positive and vice versa. The Sign gives the direction of variation ...
Distribution are a set of values or data points which the variable takes and we can easily represent as vectors in the vector space. For vectors covariance is defined as the dot product of two vectors. The value of covariance can vary from positive infinity to negative infinity. If the two...