1 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory.

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Presentation transcript:

1 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory

2 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory

3 Networks without hidden units are very limited in the input-output mappings they can model. –More layers of linear units do not help. Its still linear. –Fixed output non-linearities are not enough We need multiple layers of adaptive non-linear hidden units. This gives us a universal approximator. But how can we train such nets? –We need an efficient way of adapting all the weights, not just the last layer. This is hard. Learning the weights going into hidden units is equivalent to learning features. –Nobody is telling us directly what hidden units should do.

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12 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory 4.The actual output of the network is compared to expected output for that particular input. This results in an error value.. The connection weights in the network are gradually adjusted, working backwards from the output layer, through the hidden layer, and to the input layer, until the correct output is produced. Fine tuning the weights in this way has the effect of teaching the network how to produce the correct output for a particular input, i.e. the network learns.

13 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory The delta rule is often utilized by the most common class of ANNs called backpropagational neural networks.

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17 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory 1.A set of examples for training the network is assembled. Each case consists of a problem statement (which represents the input into the network) and the corresponding solution (which represents the desired output from the network). 2.The input data is entered into the network via the input layer. 3.Each neuron in the network processes the input data with the resultant values steadily "percolating" through the network, layer by layer, until a result is generated by the output layer.

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46 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory The error surface itself is a hyperparaboloid but is seldom smooth. Indeed, in most problems, the solution space is quite irregular with numerous pits and hills which may cause the network to settle down in a local minimum which is not the best overall solution.

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