L13NNetworks - LECTURE NOTES cse352 Professor Anita...

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LECTURE NOTES cse352 Professor Anita Wasilewska NEURAL NETWORKS Backpropagation Algorithm
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Neural Networks Classification Introduction INPUT : classification data, i.e. data that contains a classification (class) attribute. WE also say that the class label is known for all data. DATA is divided, as in any classification problem, into TRAINING and TEST data sets.
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Neural Networks Classifier ALL DATA must be normalized, i.e. all values of attributes in the dataset has to be changed to contain values in the interval [0,1], or [-1,1]. TWO BASIC normalization techniques: –Max- Min normalization and –Decimal Scaling normalization.
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Data Normalization Max-Min Normalization Performs a linear transformation on the original data. • Given an attribute A, we denote by min A, max A the minimum and maximum values of the values of the attribute A. Max-Min Normalization maps a value v of A to v’ in the range [ new_min A , new_max A] as follows.
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A new A A A A A v v min _ ) _ max _ ( ' Data Normalization Max- Min normalization formula is as follows: Example: we want to normalize data to range of the interval [-1,1] . We put: new_max A= 1 , new_minA = -1 . In general, to normalize within interval [a,b] , we put: new_max A= b , new_minA = a.
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Example of Max-Min Normalization A new A A A A A v v min _ ) _ max _ ( ' Max- Min normalization formula Example: We want to normalize data to range of the interval [0,1]. We put: new_max A= 1, new_minA =0. Say, max A was 100 and min A was 20 ( That means maximum and minimum values for the attribute A). Now, if v = 40 ( If for this particular pattern , attribute value is 40 ), v’ will be calculated as , v’ = (40-20) x (1-0) / (100-20) + 0 => v’ = 20 x 1/80=1/4 => v’ = 0.25
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Decimal Scaling Normalization Normalization by decimal scaling normalizes by moving the decimal point of values of attribute A. A value v of A is normalized to v’ by computing j v v 10 ' where j is the smallest integer such that max|v’|<1. Example : A – values range from -986 to 917. Max |v| = 986. v = -986 normalize to v’ = -986/1000 = -0.986
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Neural Network • Neural Network is a set of connected INPUT/OUTPUT UNITS , where each connection has a WEIGHT associated with it. – Neural Network learning is also called CONNECTIONIST learning due to the connections between units. • Neural Network is always fully connected. • It is a case of SUPERVISED, INDUCTIVE or CLASSIFICATION learning.
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Neural Network Learning • Neural Network learns by adjusting the weights so as to be able to correctly classify the training data and hence, after testing phase, to classify unknown data. • Neural Network needs long time for training. Neural Network has a high tolerance to noisy and incomplete data.
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Neural Network Learning Learning is being performed by a back propagation algorithm. The inputs are fed simultaneously into the input layer.
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L13NNetworks - LECTURE NOTES cse352 Professor Anita...

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