leeralgoritmes
Learning algorithms, also referred to as inductive learning algorithms or machine learning algorithms, are computational procedures that enable computers to infer patterns and make predictions from data. They operate by identifying statistical regularities in input data and generalizing these patterns to unseen instances. Classic categories of learning algorithms include supervised learning, unsupervised learning, semi‑supervised learning, and reinforcement learning, each suited to different problem settings. Supervised algorithms, such as linear regression, decision trees, support vector machines, and neural networks, learn from labeled examples. Unsupervised methods, such as k‑means clustering, hierarchical clustering, and principal component analysis, discover hidden structure in unlabeled data. Semi‑supervised algorithms blend both labeled and unlabeled data to improve learning efficiency. Reinforcement learning algorithms, like Q‑learning and policy gradients, learn optimal actions through interaction with an environment and feedback signals.
The development of learning algorithms has been driven by advances in statistical theory, computational power, and
Applications of learning algorithms span sectors including finance for fraud detection, healthcare for diagnostic support, marketing