Recognition Using ANN with Engineered Features
The three pillars for a successful ML application are the data, features, and model. They should cope with each other. The most relevant features that differentiate among the different cases existing in the data are used. Representative features are critical in building an accurate ML application. They should be accurate enough to work well under different conditions such as a change in scale and rotation. Such features should work well with the selected ML model. You shouldn’t use more features than needed, because this adds more complexity to the model. Feature selection and reduction techniques are used to find the minimum set of features to build an accurate model.