MATLAB: Early stopping neural network:: why data divided in two three subset??

MATLABneural network

dear

as i know from wiki and book by Simon hakin that early stopping use two type sets of data the first one for training and second one for validation test so which may ask

why their are three curves in performance plot when we use matlab train function ??? the curve name train error,validation and test error ???

Best Answer

Data = Design + Non-design

Design = Train(estimate weights) + Validation(Stop training when MSEval goes thru a minimum).

Non-design = Test(Obtain UNBIASED generalization estimate of performance on unseen non-design data).

Related Solutions

MATLAB: Neural nets: validation vs testing

NOTE THE DIFFERENCE BETWEEN DESIGN AND TRAINING

1. Data Division

 DATA = TRAINING + VALIDATION + TESTING + UNSEEN

2. Design

 DATA      = DESIGN + NONDESIGN
 DESIGN    = TRAINING + VALIDATION
 NONDESIGN = TESTING + UNSEEN

3. Training

 DATA        = TRAINING + NONTRAINING
 NONTRAINING = VALIDATION + TESTING + UNSEEN

4. Training data is used to directly modify weight and bias values. Performance estimates obtained from training data are HIGHLY BIASED because the same data is directly used for both modifications and evaluation.

5. Validation data is used for

   a. ValStopping: Stops training when nontraining    
      validation error increases for m consecutive   
      epochs. This enhances the network's ability to 
      generalize to nondesign ( i.e., testing and 
      unseen) data. The MATLAB default is m = 6.
   b. Model ranking: Ranks multiple designs w.r.t. 
      performance. Performance estimates are SLIGHTLY 
      BIASED because validation data indirectly 
      affects design.

6. Testing data is used to obtain UNBIASED ESTIMATES OF NONTRAINING (including UNSEEN) DATA.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Fitnet Why test results are unbiased

This discussion has absolutely nothing to do with bias weights.

Theoretically, training, validation and test data are all assumed to be random samples from the same probability distribution function.

Both training and validation data are used to design the net with the goal of minimizing a performance function (such as mse (fitnet) or crossentropy (patternnet) for net performance on ANY data (seen and unseen) obtained from the same probability distribution.

Therefore, their performance cannot be used to obtain an unbiased (i.e., honest) estimate of performance on unseen data.

On the other hand, the test data is in no way involved in the design of the net. Therefore it is a valid representative of "unseen" data and it's performance is considered an unbiased estimate of net performance on unseen data.