Neural-network terminology
Section of Neural units, part of Statistical learning
Neural-network terminology
| Type | Section |
| Taught in | Neural units |
| Part | Statistical learning |
The next two pages differentiate these units and then train them, so the names of the weights stay fixed.
| Symbol | Meaning |
|---|---|
| i=1,\ldots,n | observations (patterns); n_{\mathrm{tr}} training patterns, n_{\mathrm{te}} test patterns, with n_{\mathrm{tr}}+n_{\mathrm{te}}=n |
| j=1,\ldots,p | inputs (attributes) |
| k=1,\ldots,m | outputs (responses) |
| q=1,\ldots,l | hidden units |
| x_{ij}, y_{ik} | input j and correct output k of pattern i |
| h_{iq}, o_{ik} | hidden unit q and output unit k for pattern i; o_{ik}=f_{\mathrm{network},k}(x_i) |
| w_{jq}, w_{qk} | weight from input j to hidden unit q; from hidden unit q to output k |
| u_{iq}=\sum_{j=0}^{p} w_{jq} x_{ij} | activation of hidden unit q for pattern i |
| z_{ik}=\sum_{q=0}^{l} w_{qk} h_{iq} | activation of output unit k for pattern i |
| g(\cdot) | unit activation, for example g(x)=1/(1+e^{-x}) |
| w_{0b}, w_{0c} | bias of hidden unit b and of output unit c; the extra input x_{i0} and the extra hidden unit h_{i0} are fixed at +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 without a bias