Threshold unit without a bias
Section of Neural units, part of Statistical learning
Threshold unit without a bias
| Type | Section |
| Taught in | Neural units |
| Part | Statistical learning |
With w_0=0 the boundary is \{x:\sum_j x_j w_j=0\}, which passes through the origin. A separator that ought to miss the origin cannot be represented. For w=(1,-2)^\top the boundary is x_1-2x_2=0, the line x_2=\tfrac12 x_1. The weights (0.5,1), (1,1) and (2,1) instead give the three lines x_2=-x_1/2, x_2=-x_1 and x_2=-2x_1.
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 with a bias