Heuristic Learning Model-Based Stochastic Regularization Technique for Reducing the Overfit of Training Data

Researchers may have learned about how to build and develop feedforward neural networks. The moment we develop a network, there is the question of training and testing the dataset along with the machine learning algorithm. We have to test our algorithm not only on the training set but also on the testing set with respect to its fit. When an algorithm performs well on the training set but performs poorly on the testing set, the algorithm is said to be overfitted on the training data. In short, there is a need to focus on reducing the overfitting. To deal with this problem, we have to make our stochastic model generalize over the training data using the emphasized regularization technique. Here, the term stochastic means the outcome derived from a random event or random process; the terms referred to as “Stochastic” and Regularization mean minimizing the error of the testing set directly proportional to increasing the error rate of the training set. Many such regularization techniques are invented by the research practitioner, but minimal evidence has been observed through types of learning rules. So to come up with a more effective regularization technique is the need of the hour. In order to achieve this, there needs to be put some extra constraints, parameter-specific values, changes in learning rate, and changes in activation function; into the machine learning model. If it is computed and chosen correctly, then it helps to reduce testing error. Finally in order to develop such a machine learning model; researchers have to look into the strengths and weaknesses of the model, new or derived theory, synthesis of existing theory along with the support of other derived theories and the gaps to come up with new models.