半教師あり学習
セミスーパーバイズド学習は 機械学習のアプローチ that combines a small amount of ラベル付きデータ with a large amount of unlabeled data during the training process. It sits between 教師あり学習, where all 訓練データ ラベル付けされたデータと、ラベル付けされたデータを使用しない教師なし学習を組み合わせたものである。
In many real-world scenarios, acquiring labeled data can be expensive and time-consuming, while unlabeled data is often abundant. Semi-supervised learning aims to leverage this wealth of unlabeled data to improve the learning accuracy of models. The fundamental idea is that even though unlabeled data does not provide explicit target values, it can still contain valuable information about the underlying structure of the データ分布.
A common approach in semi-supervised learning is to use a model trained on the labeled data to make predictions on the unlabeled data. These predicted labels can then be used to refine the model further. Techniques such as self-training, co-training, and graph-based methods are often employed to facilitate this process. For instance, self-training iteratively adds high-confidence predictions for unlabeled data to the training set, while co-training involves training two models on different views of the data, allowing them to teach each other.
この方法は、さまざまなアプリケーションで広く使用されている。 自然言語処理, image classification, and speech recognition, where labeled data can be scarce but unlabeled data is plentiful. By effectively utilizing both types of data, semi-supervised learning can lead to improved performance and generalization of machine learning models.