Linear Separability

Appears in 1 paper

A property of a dataset: two classes are linearly separable if you can draw a straight line (in 2D) or a flat hyperplane (in higher dimensions) that perfectly separates all examples of one class from all examples of the other.

As used in Paper 02 — The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain →

A property of a dataset: two classes are linearly separable if you can draw a straight line (in 2D) or a flat hyperplane (in higher dimensions) that perfectly separates all examples of one class from all examples of the other. The Perceptron can only learn linearly separable problems.

Paper 02 — The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain →