difference between fitcnet and patternnet functions

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I am not able to get difference between fitcnet and patternnet functions; when to use which one and what change happens in the result, if one replaced by other?

Accepted Answer

Girijashankar Sahoo
Girijashankar Sahoo on 20 May 2021
1. FITNET for regression (MATLAB calls it curve fitting) which is supposed to be a replacement for NEWFF)
2. PATTERNNET for pattern recognition and classification ( which were previously achieved using NEWFF)
Yogini Prabhu
Yogini Prabhu on 20 May 2021
Train neural network 'classification' model
Use fitcnet to train a feedforward, fully connected neural network for classification. The first fully connected layer of the neural network has a connection from the network input (predictor data), and each subsequent layer has a connection from the previous layer. Each fully connected layer multiplies the input by a weight matrix and then adds a bias vector. An activation function follows each fully connected layer. The final fully connected layer and the subsequent softmax activation function produce the network's output, namely classification scores (posterior probabilities) and predicted labels. For more information, see Neural Network Structure.

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