Threshold unit with a bias
Section of Neural units, part of Statistical learning
Threshold unit with a bias
| Type | Section |
| Taught in | Neural units |
| Part | Statistical learning |
The bias is the constant term that frees the boundary from the origin. With it, a threshold unit can realise any linear separator. For w=(1,1)^\top the boundary is x_1+x_2+w_0=0, and changing w_0 translates that line. In general the boundary is the hyperplane \{x:w_0+w^\top x=0\}, which has normal vector w, and the unit outputs 1 on the side the normal points to. The distance from a point to this hyperplane is derived with the support vector machine; the same normal is the object that derivation starts from.
Used in
- Taught in Neural units
- All 7 questions on Neural units
See also
Artificial neuron · Perceptron learning rule · A single threshold unit cannot compute XOR · One hidden layer approximates continuous functions · History of the neural unit · Neural unit · Linear separability · Threshold unit without a bias