MATLAB: What is the default supervised learning algorithm in matlab

neural networksupervised learning

With this code below

[x,t] = iris_dataset; net = patternnet; net = configure(net,x,t);

net = train(net,x,t); save('C:\Temp\trained_net.mat','net');

y = net(x);

perf = perform(net,t,y); display(['performance: ', num2str(perf)]);

I will assume matlab uses its default learning algorithm since i did not specify any. What is the default supervised learning algorithm in matlab?

Best Answer

All of the net properties can be deduced via

 net = net   % No semicolon

Hope this helps.

Greg

Related Solutions

MATLAB: Problem: feed-forward neural network – the connection between the hidden layer and output layer is removed.

% Hi Greg and Brendan. Thanks for your reply. % % Well, after struggling reading the Matlab documentation, % I think I understood what the problem was. % % The code I posted was just a dummy example to explain the % issue I was facing. My real problem is the following: I am % trying to solve an anomaly detection problem and, in % particular, reading sensor data, I am trying to detect when % there is an anomaly behavior. % % In order to do so, I am using different machine learning % algorithms and evaluating their performance. So far, I have % used the nearest neighbor algorithm, the self-organizing maps % and the support vector machines. Another "instrument" I would % like to use is that of neural networks.

 Your problem is that you did not do the following:

1. Identify the problem as one of the following

   a. regression/curvefitting
   b. classification/patternrecognition
   c. clustering
   d. time-series
 2.  Search both NEWSGROUP and ANSWERS using 
   a. classification 
   b. pattern-recognition
   to identify 
   a. classification/pattern-recognition functions 
      (e.g., patternnet)
   b. example classification/pattern-recognition code 
      and data examples 
 3. Practice using one or more of the MATLAB classification/...
    pattern-recognition example data obtained from 
   help nndata
   doc  nndata

5. Apply what is learned above on your dataset.

% My idea was to train the neural network with normal data % (so, a one-class data set) and use the net to compute a sort % of anomaly score. But, if I got it right, it has no sense to % train a neural network having just one output neuron with % data belonging to just one class. Neural networks are very % variegated and represent a vast subject. I am slowly learning % them.

 No. Create an input and target sets for 2 classes.

% As regard the default settings, I have just modified the % algorithm in order to have the classic gradient descent.

 No. Always begin using as many defaults as possible. Then 
consider (one-at-a-time) replacing default settins.

% P.s. Little question: when there is no target data defined, % isn't the problem an unsupervised machine learning problem? % Theorically, I could do it (given that my data set is not a % one-class data set).

 Your best bet is to create a 2-class dataset.

Hope this helps.

Greg

MATLAB: In the Neural Network Toolbox, how can I set different trainParam values for each layer of the network

From examples of regression using fitnet([ 5 5 ] ) and classification using patternnet([5 5]) I've deduced the following:

1. The learning rate, lr, and momentum constant, mc, can be specified for each set of layer weights between layer i and layer j.

2. For fitnet with default TRAINLM, the training properties listed in net.trainParam are fixed for all layer weights. In particular, mu, mu_dec, mu_inc and mu_max.

3. For patternnet with default TRAINSCG, the training properties listed in net.trainParam are fixed for all layer weights. In particular, sigma and lambda.

For example

          clear all, clc
          [x,t] = simpleclass_dataset;
          [I N ] = size(x)
          [ O N ] = size(t)
          net = patternnet([ 5 5 ]);
          rng(0)
          net = configure(net,x,t)
          % ---SNIP
          layerWeights =  net.layerWeights
        %    layerWeights = 
        % 









        %                   []                  []    []
        %     [1x1 nnetWeight]                  []    []
        %                   []    [1x1 nnetWeight]    []
        layerWeights21 = layerWeights{2,1}
        % layerWeights21 = 
        % 
        %     Neural Network Weight
        % 
        %             delays: 0
        %            initFcn: (none)
        %       initSettings: .range
        %              learn: true
        %           learnFcn: 'learngdm'
        %         learnParam: .lr, .mc
        %               size: [5 5]
        %          weightFcn: 'dotprod'
        %        weightParam: (none)
        %           userdata: (your custom info)
        % 
          layerWeights21learnParam = layerWeights21.learnParam
        % layerWeights21learnParam = 
        %  
        %     Function Parameters for 'learngdm'
        % 
        %     Learning Rate     lr: 0.01
        %     Momentum Constant mc: 0.9     
        % So, the specifications have the form
        % 
        % net.layerWeights{ 2,1 }.learnParam.lr  = lr21 ;
        % net.layerWeights{ 3, 2 }.learnParam.lr = lr23 ;
        % 
        % net.layerWeights{ 2,1 }.learnParam.mc = mc21 ;
        % net.layerWeights{ 3, 2 }.learnParam.mc = mc32
        trainParam = net.trainParam
        % trainParam = 
        % 
        % 
        %     Function Parameters for 'trainscg'
        % 
        %     Show Training Window Feedback   showWindow: true
        %     Show Command Line Feedback showCommandLine: false
        %     Command Line Frequency                show: 25
        %     Maximum Epochs                      epochs: 1000
        %     Maximum Training Time                 time: Inf
        %     Performance Goal                      goal: 0
        %     Minimum Gradient                  min_grad: 1e-006
        %     Maximum Validation Checks         max_fail: 6
        %     Sigma                                sigma: 5e-005
        %     Lambda                              lambda: 5e-007
        %
        %    Resulting in specifications of the form
        net.trainParam.sigma = sigma0 ;
        net.trainParam.lamda= lambda0 ;

Hope this helps.

Thank you for formally accepting my answer.

Greg.

Best Answer

Related Solutions

MATLAB: Problem: feed-forward neural network – the connection between the hidden layer and output layer is removed.

MATLAB: In the Neural Network Toolbox, how can I set different trainParam values for each layer of the network

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