Mappe auto-organizzanti

Identificare i vettori prototipo per i cluster di esempi, le distribuzioni degli esempi e le relazioni di somiglianza tra i cluster

App

Risolvere il problema del clustering utilizzando reti di mappe auto-organizzanti (SOM)

`nctool`	Open Neural Net Clustering app
`view`	Visualizzazione della rete neurale superficiale

selforgmap Mappa auto-organizzante

`train`	Addestra una rete neurale superficiale
`plotsomhits`	Traccia i successi di campionamento della mappa auto-organizzante
`plotsomnc`	Plot self-organizing map neighbor connections
`plotsomnd`	Plot self-organizing map neighbor distances
`plotsomplanes`	Plot self-organizing map weight planes
`plotsompos`	Plot self-organizing map weight positions
`plotsomtop`	Traccia la topologia della mappa auto-organizzante
`genFunction`	Generate MATLAB function for simulating shallow neural network

Data clustering con una mappa auto-organizzante
Raggruppare i dati per similarità utilizzando l’app Neural Net Clustering o le funzioni della riga di comando.
Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB^® tools.
Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.
Iris Clustering
This example illustrates how a self-organizing map neural network can cluster iris flowers into classes topologically, providing insight into the types of flowers and a useful tool for further analysis.
Gene Expression Analysis
This example demonstrates looking for patterns in gene expression profiles in baker's yeast using neural networks.
One-Dimensional Self-Organizing Map
Neurons in a 2-D layer learn to represent different regions of the input space where input vectors occur.
Two-Dimensional Self-Organizing Map
As in one-dimensional problems, this self-organizing map will learn to represent different regions of the input space where input vectors occur.

Cluster with Self-Organizing Map Neural Network
Use self-organizing feature maps (SOFM) to classify input vectors according to how they are grouped in the input space.