基礎

Supervised Learning

定義

A machine learning approach where the model is trained on labelled input-output pairs. The model learns to map inputs to correct outputs, enabling predictions on new, unseen data. Most commercial ML models in production today are supervised.

関連用語

Machine Learning (ML)

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. ML algorithms build models based on training data to make predictions or decisions.

Unsupervised Learning

A machine learning approach that discovers structure in unlabelled data. Common techniques include clustering (grouping similar items) and dimensionality reduction. It is often used for anomaly detection and exploratory data analysis.

Training (AI)

The process of exposing a model to large volumes of labelled or unlabelled data and adjusting its internal parameters to minimise prediction errors. Training is computationally intensive, often performed on GPU clusters, and constitutes the major cost in foundation model development.

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