Feature Vectors

It frequently happens that we can measure a fixed set of d features for any object or event that we want to classify. For example, we might always be able to measure
x1 = area
x2 = perimeter
xd = arc_length / straight_line_distance
In this case, we can think of our feature set as a feature vector x, where x is the d-dimensional column vector

Equivalently, we can think of x as being a point in a d-dimensional feature space. By this process of feature measurement, we can represent an object or event abstractly as a point in feature space.

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