MATLAB: Data classification: Learning vector Quantization or two-layer feed-forward network??

data classification

hello,

I'm experimenting how to to use neural networks for data classification using the iris flowers data set.

If you type 'nprtool' in the command line you can load the iris flowers data set and it will classify it using a two-layer feed-forward network, with sigmoid hidden and output neurons. Its classifies 98% correctly.

Mathworks also say that you can use a Learning Vector Quantization (LVQ) network to classify data also. There is a separate example here <http://www.mathworks.co.uk/support/solutions/en/data/1-5RDETB/index.html?product=NN&solution=1-5RDETB>

and they also demonstrate LVQ with the iris flowers data set here http://www.mathworks.co.uk/help/toolbox/nnet/ref/lvqnet.html

Using LVQ it only classifies 90-92% correctly.

My question is, why is there difference in the amount classified correctly? and ultimately which is the best method to use?

Thank you

Best Answer

LVQ is not self organizing. It is created using supervised learning. The FFMLP has a different topology and has been proven to be a universal approximator. The LVQ topology is similar to that of the RBF. The RBF has been proven to be a universal approximator. The shortcomings of LVQ1 have been addressed by LVQ2. However, it, also, is not a universal approximator.

Hope this helps.

Greg

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MATLAB: How to cluster training data before training a neural network

Could you explain further? There are a number of ways to cluster your data:

Statistics Toolbox:

http://www.mathworks.com/help/stats/unsupervised-learning.html

Neural Network Toolbox:

http://www.mathworks.com/help/nnet/ug/cluster-with-self-organizing-map-neural-network.html

MATLAB: Matlab code for Supervised SOM neural network

A net cannot be self-organizing if it is supervised. You should be looking for Learning Vector Quantization Nets

>> lookfor lvq

 nnd14lv1                       - NND4LV1 LVQ1 demonstration.
 nnd14lv2                       - NND4LV1 LVQ2 demonstration.
 initlvq                        - LVQ weight initialization function.
 learnlv1                       - LVQ1 weight learning function.
 learnlv2                       - LVQ 2.1 weight learning function.
 lvqnet                         - Learning vector quantization (LVQ) neural network.
 lvqoutputs                     - Define settings for LVQ outputs, without changing values.
 newlvq                         - Create a learning vector quantization network.
 nnt2lvq                        - Update NNT 2.0 learning vector quantization network.

Thank you for formally accepting my answer

Greg

Best Answer

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MATLAB: How to cluster training data before training a neural network

MATLAB: Matlab code for Supervised SOM neural network

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