SVM has a learning rate and expansion rate which takes care of this. The learning rate compensates or penalises the hyperplanes for making all the wrong moves and expansion rate deals with finding the maximum separation area between classes.

SVM (Support Vector Machine) is a supervised learning algorithm primarily used for classification tasks. It doesn’t inherently perform self-learning, which typically refers to a process where a model iteratively improves itself by learning from its own predictions and data it generates. However, SVM can be integrated into a self-learning system as a component for classification tasks.

If the question is specifically about how SVM can be used in a self-learning system, you could answer by explaining how SVM could be employed within a larger framework where self-learning mechanisms are implemented. For example:

Active Learning: SVM can be used in an active learning setting where the model actively selects the most informative data points to label from an unlabeled dataset, improving its performance with each iteration.
Online Learning: SVM can be adapted for online learning scenarios where it continuously updates its model parameters as new data becomes available. This can simulate a form of self-learning where the model adapts to changing environments.
Reinforcement Learning: In a reinforcement learning setup, SVM can be utilized as a function approximator to estimate the value function or policy in certain settings.
Semi-supervised Learning: SVM can also be used in semi-supervised learning setups where a limited amount of labeled data is available along with a large pool of unlabeled data. The model can iteratively incorporate the information from both labeled and unlabeled data to improve its performance.

In summary, while SVM itself doesn’t perform self-learning, it can be utilized within frameworks or methodologies that incorporate self-learning mechanisms to enhance its performance in various applications.