Introduction Metric learning is a subfield of machine learning that focuses on learning a distance function that quantifies the similarity or dissimilarity between data points. Unlike traditional machine learning models that typically use fixed, pre-defined metrics (such as Euclidean distance), metric learning aims to learn the best metric that captures the underlying structure of the
Introduction Semi-supervised learning (SSL) is a machine learning paradigm that combines both labeled and unlabeled data to improve the learning process. In traditional supervised learning, models are trained on a fully labeled dataset, where each input comes with a corresponding output. However, obtaining labeled data is often expensive, time-consuming, and labor-intensive, especially in complex domains.
Introduction Bayesian Networks (BNs) are probabilistic graphical models that represent a set of variables and their conditional dependencies using a directed acyclic graph (DAG). These models provide a way of representing complex relationships in data through conditional probabilities. Bayesian Networks have been widely used in various fields such as artificial intelligence (AI), machine learning (ML),