L13NNetworks

# L13NNetworks - LECTURE NOTES cse352 Professor Anita...

This preview shows pages 1–11. Sign up to view the full content.

LECTURE NOTES cse352 Professor Anita Wasilewska NEURAL NETWORKS Backpropagation Algorithm

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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.
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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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.
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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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
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

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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.
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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Neural Network Learning Learning is being performed by a back propagation algorithm. The inputs are fed simultaneously into the input layer.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 44

L13NNetworks - LECTURE NOTES cse352 Professor Anita...

This preview shows document pages 1 - 11. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online