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Semi-supervised Learning
Semisupervised learning is a machine learning technique that combines both labeled and unlabeled data to improve the accuracy of a model. In this approach, a small portion of the data is labeled, while the majority remains unlabeled. By leveraging the information from both labeled and unlabeled data, semisupervised learning aims to achieve better performance compared to traditional supervised learning methods.
To illustrate the concept of semisupervised learning, let’s consider a natural language processing example. Imagine we have a large dataset of customer reviews for a product, where each review is labeled as positive or negative. However, labeling each review manually can be time-consuming and expensive. Therefore, we only have a small subset of labeled reviews, while most of the dataset remains unlabeled.
In a traditional supervised learning approach, we would train a model using only the labeled reviews. However, this limited labeled data may not capture the full complexity and diversity of the language used in customer reviews. This can lead to a model that cannot generalize well to unseen data.
In contrast, semisupervised learning allows us to leverage the vast amount of unlabeled reviews to improve the model’s performance. By incorporating the unlabeled data…
