Main Content

Pattern Recognition

Train a neural network to generalize from example inputs and their classes, train autoencoders

Applications

Neural Net Pattern RecognitionSolve pattern recognition problem using two-layer feed-forward networks

Classes

AutoencoderAutoencoder class

Fonctions

développer tout

nprtoolOpen Neural Net Pattern Recognition app
viewView shallow neural network
trainAutoencoderTrain an autoencoder
trainSoftmaxLayerTrain a softmax layer for classification
decodeDecode encoded data
encodeEncode input data
predictReconstruct the inputs using trained autoencoder
stackStack encoders from several autoencoders together
networkConvert Autoencoder object into network object
patternnetGenerate pattern recognition network
lvqnetLearning vector quantization neural network
trainTrain shallow neural network
trainlmLevenberg-Marquardt backpropagation
trainbrBayesian regularization backpropagation
trainscgScaled conjugate gradient backpropagation
trainrpResilient backpropagation
mseMean squared normalized error performance function
rocReceiver operating characteristic
plotconfusionPlot classification confusion matrix
ploterrhistPlot error histogram
plotperformPlot network performance
plotregressionPlot linear regression
plotrocPlot receiver operating characteristic
plottrainstatePlot training state values
crossentropyNeural network performance
genFunctionGenerate MATLAB function for simulating shallow neural network

Exemples et procédures

Basic Design

Training Scalability and Efficiency

Optimal Solutions

Classification

  • Crab Classification
    This example illustrates using a neural network as a classifier to identify the sex of crabs from physical dimensions of the crab.
  • Wine Classification
    This example illustrates how a pattern recognition neural network can classify wines by winery based on its chemical characteristics.
  • Cancer Detection
    This example shows how to train a neural network to detect cancer using mass spectrometry data on protein profiles.
  • Character Recognition
    This example illustrates how to train a neural network to perform simple character recognition.

Autoencoders

Concepts