Which type of learning is typically employed when there's neither complete supervision nor complete absence of supervision, but a mix where an agent learns to act in an environment?
- Reinforcement Learning
- Self-supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
Semi-supervised Learning fits this scenario. It combines labeled and unlabeled data to train a model. In situations where you have some labeled data but not enough for full supervision, or when labeling is expensive, semi-supervised learning is a practical choice.
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