| Neural Network Toolbox | |
Self-Organizing Maps
A competitive network learns to categorize the input vectors presented to it. If a neural network only needs to learn to categorize its input vectors, then a competitive network will do. Competitive networks also learn the distribution of inputs by dedicating more neurons to classifying parts of the input space with higher densities of input.
A self-organizing map learns to categorize input vectors. It also learns the distribution of input vectors. Feature maps allocate more neurons to recognize parts of the input space where many input vectors occur and allocate fewer neurons to parts of the input space where few input vectors occur.
Self-organizing maps also learn the topology of their input vectors. Neurons next to each other in the network learn to respond to similar vectors. The layer of neurons can be imagined to be a rubber net that is stretched over the regions in the input space where input vectors occur.
Self-organizing maps allow neurons that are neighbors to the winning neuron to output values. Thus the transition of output vectors is much smoother than that obtained with competitive layers, where only one neuron has an output at a time.
| | Supplemental LVQ2.1 Learning Rule (learnlv2) | Learning Vector Quantization Networks | |
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