Manuscript Number : IJSRST184935
Cancer Data Classification Using Clustering Techniques
Authors(2) :-Dr. V. Umadevi, P. Deepalakshmi In a multilayered feedforward network, neurons are organized into layers. The input layer is not fully composed of neurons, but rather it consists of some values in a data record, that constitutes inputs to the next layer of neurons. The next layer is called a hidden layer; there may be many hidden nodes. The concluding layer is the output layer, there is only one node for each class. A single forward pass through the network results in the assignment of a value to each output node, and the record is assigned to whichever classifications node had the highest value. Multilayer feedforward networks are trained using the Backpropagation (BP) learning algorithm. Backpropagation training algorithm when applied to a feedforward multilayer neural network then it is known as Backpropagation neural network. Functional signals flows in the forward path and error signals transmit in backward path. That's why it is Error Backpropagation or shortly backpropagation network. The activation function that can be differentiated (such as sigmoid activation function) is chosen for hidden and output layer computational neurons. The algorithm is based on an error-correction rule. Learning is based upon mean squared error and generalized delta rule. The rule applied for weight updation is generalized delta rule.
Dr. V. Umadevi Backpropagation Neural Network , Clustering, Classification, Cancer Data Publication Details
Published in : Volume 4 | Issue 9 | July-August 2018 Article Preview
Research Director, Department of Computer Science, Jairams Arts and Science College, Karur, Tamil Nadu, India
P. Deepalakshmi
Research Scholar, Department of Computer Science, Jairams Arts and Science College, Karur, Tamil Nadu, India
Date of Publication : 2018-07-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 152-162
Manuscript Number : IJSRST184935
Publisher : Technoscience Academy
Journal URL : https://ijsrst.com/IJSRST184935
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