Semi Supervised Learning: What It's, Why Significant , How it Works, Types, Applications, Advantages, Disadvantages and Strategies ! Embrace AI to Leverage Your Intelligence in Innovation !!
Abstract: Semi-supervised learning is a machine learning technique that uses a combination of labeled and unlabeled data to train artificial intelligence (AI) models: How it works Semi-supervised learning uses a small amount of labeled data and a large amount of unlabeled data to train models. The unlabeled data helps improve the performance of the learning process. When it's useful Semi-supervised learning is especially useful when it's difficult or expensive to obtain enough labeled data, but large amounts of unlabeled data are easy to get. How it's used Semi-supervised learning can be used for tasks like identifying fraud and classifying web content. How it's based on assumptions Semi-supervised learning methods are based on assumptions about the underlying data distribution, such as the smoothness assumption that similar data points have the same label. Techniques Some techniques used in semi-supervised learning include consistency regular