What is semi-supervised learning?

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This is a podcast episode titled, What is semi-supervised learning?. The summary for this episode is: This episode of Techsplainers explores semi-supervised learning, the machine learning approach that bridges supervised and unsupervised techniques by combining small amounts of labeled data with larger volumes of unlabeled information. The episode explains why this method is crucial when obtaining fully labeled datasets is prohibitively expensive or time-consuming, such as in medical imaging or genetic analysis. We break down the key assumptions that make semi-supervised learning work—including the cluster assumption, smoothness assumption, low-density assumption, and manifold assumption—and how they help models generalize beyond limited labeled examples. The discussion covers major implementation approaches, including transductive methods like label propagation, and inductive methods like wrapper techniques, unsupervised pre-processing, and intrinsically semi-supervised algorithms. Real-world applications and challenges are also examined, providing listeners with a comprehensive understanding of this practical machine learning technique. Find more information at <a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a>. Narrated by Anna Gutowska

DESCRIPTION

This episode of Techsplainers explores semi-supervised learning, the machine learning approach that bridges supervised and unsupervised techniques by combining small amounts of labeled data with larger volumes of unlabeled information. The episode explains why this method is crucial when obtaining fully labeled datasets is prohibitively expensive or time-consuming, such as in medical imaging or genetic analysis. We break down the key assumptions that make semi-supervised learning work—including the cluster assumption, smoothness assumption, low-density assumption, and manifold assumption—and how they help models generalize beyond limited labeled examples. The discussion covers major implementation approaches, including transductive methods like label propagation, and inductive methods like wrapper techniques, unsupervised pre-processing, and intrinsically semi-supervised algorithms. Real-world applications and challenges are also examined, providing listeners with a comprehensive understanding of this practical machine learning technique.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska