Possible improvements and potential applications

We have illustrated how to build an ANN, feed it training data, and use it for classification. There are a number of aspects we can improve, depending on the task at hand, and a number of potential applications of our new-found knowledge.

Improvements

There are a number of improvements that can be applied to this approach, some of which we have already discussed:

• For example, you could enlarge your dataset and iterate more times, until a performance peak is reached

• You could also experiment with the several activation functions (cv2.ml.ANN_MLP_SIGMOID_SYM is not the only one; there is also cv2.ml.ANN_MLP_IDENTITY and cv2.ml.ANN_MLP_GAUSSIAN)

• You could utilize different training flags (cv2.ml.ANN_MLP_UPDATE_WEIGHTS, cv2.ml.ANN_MLP_NO_INPUT_SCALE, cv2.ml.ANN_MLP_NO_OUTPUT_SCALE), and training methods (back propagation or resilient back propagation)

Aside from that, bear in mind one of the mantras of software development: there is no single best technology...