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Semi-supervised Learning

The semi-supervised learning algorithm is one of the machine learning models, learning the patterns from labeled and unlabeled data to perform certain learning tasks. The main goal of semi-supervised learning is to address the constraints in both supervised and unsupervised learning models. A Semi-supervised learning algorithm is a stable algorithm that builds a learning model with a small number of labeled patterns and a huge number of unlabeled patterns in the input samples. The Semi-supervised learning algorithm is categorized into two types, involves semi-supervised classification and semi-supervised clustering. Assumptions of semi-supervised learning models are Continuity or smoothness assumption: Closely related input points should have same output label, Cluster assumption: Formation of different clusters and points in the same cluster should have the same label. Low-density assumption: The crossing of the decision boundary should be in a low-density area. Manifold assumption: The data points lie on the lower-dimensional manifolds, and data points in the same manifold should have the same label. Semi-supervised learning methods are generative models, low-density separation, Laplacian regularization, and heuristic approach. In the real world, internet content classification, protein sequence classification, text document classification, and speech analysis. Application fields are often deal with the lack of adequate labeled data for decision-making. In this context, the semi-supervised learning model plays a significant role in real-time decision-making for the inadequate labeled data. Furthermore, computer-aided diagnosis, drug discovery, and text processing are semi-supervised learning-based application fields. Recent advances in semi-supervised learning are semi-supervised learning models in automated machine learning with meta-learning and neural architecture, semi-supervised based software package development, and semi-supervised neural networks.