Semi-Supervised Learning: Training AI on Limited Labels to Unlock Value in Big Data
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Semi-supervised learning trains models using a small set of labeled data and a large set of unlabeled data, which is cheaper than fully supervised learning.
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It relies on assumptions like continuity and cluster assumptions to infer labels for unlabeled data based on its similarity to labeled data.
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Applications include computer vision, NLP, anomaly detection, classification tasks, etc.
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It helps deal with the data deluge from user-generated content on social media platforms.
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Semi-supervised learning makes training AI models more efficient and accessible in the age of big data.