Activity 16: Neural Networks

Similar to the previous activity, Activity 16 is an extension of Activity 14. Here, we again use the features that we were able to extract in activity 14. However, unlike activity 14 and 15 where we used minimum distance classification and least discriminant analysis to determine class membership, in this activity what we'll use is neural networks.

A neural network is a computational model of how neurons in the brain work. It is one of the preferred method in pattern recognition for one does not need heuristics and recognition rules to perform classification. In this method, neural network "learn" the rules of mapping by example. [1]

For this activity what we want to do is to train the neural network with the training set for each class. The neural network should learn the "characteristic" feature of a specific class. To be able to perform this, we used the Artificial Neural Network toolbox for Scilab. We utilized the code posted in Cole Fabros' AP186 blog. Assigning the learning rate be equal to 0.1, 0 for the error tolerated by the network run for 1000 cycles the results are the following..

The first column is the expected class of each test object, the second column is the assigned output assignment. This means that if the input test object belong say to the class of flower, then output should be near the assigned output assignment. As can be observed in the table below, indeed the neural network was able to classify the test objects correctly.



For this activity I give myself a grade of 10 for I was able to obtain 100% accuracy in class classification.


I thank Thirdy Buno, Miguel Sison for helping me install the Artificial Neural Network Toolbox and Cole Fabros and Jeric Tugaff, former students of AP 186, forcode utilized in this activity.


References:
[1] Activity 16: Neural Networks Manual