Supervised Learning

Supervised learning is a machine-learning approach that trains a model on a labeled dataset. The algorithm learns the relationship between inputs and outputs by minimizing a loss function. Once trained, the model can generalize to unseen data and make predictions. Common applications include classification of images and spam detection.

Japanese terms

1. Supervised learning — 教師きょうしあり学習がくしゅう: A machine-learning approach in which a model is trained on labeled examples.
2. Machine learning — 機械きかい学習がくしゅう: A field of AI in which systems learn patterns or behavior from data and experience.
3. Model — モデル: A mathematical representation that processes input data to produce predictions, classifications, or decisions.
4. Labeled dataset — ラベル付つきデータセット: A collection in which each input is paired with its expected output or target.
5. Input data — 入力にゅうりょくデータ: The raw information or features supplied to a model.
6. Output label — 出力しゅつりょくラベル: The expected category or value associated with an input.
7. Training — 訓練くんれん: The process of adjusting a model’s parameters using data.
8. Algorithm — アルゴリズム: A defined procedure used to solve a problem or perform a computation.
9. Loss function — 損失そんしつ関数かんすう: A formula that measures the difference between predicted and actual outputs.
10. Prediction error — 予測よそく誤差ごさ: The difference between a predicted value and its target value.
11. Generalization — 一般化いっぱんか: A model’s ability to perform well on data it did not see during training.
12. Unseen data — 未見みけんデータ: Data that a model did not encounter during training.
13. Prediction — 予測よそく: An output produced by a trained model.
14. Image classification — 画像がぞう分類ぶんるい: Assigning a label or category to an image.
15. Spam email detection — スパムメール検出けんしゅつ: Classifying email as spam or not spam.