01 Oct Semi-Supervised Machine Learning
We saw before that Supervised learning uses labeled data and Unsupervised uses unlabeled data. Therefore, semi-supervised learning sits in between supervised and unsupervised learning.
The Semi-Supervised uses both labeled and unlabeled data to train a model. The training data includes:
- Labeled Data (small amount)
- Unlabeled Data (large amount)
Example – Semi-Supervised Learning
- Labeled Data: A small set of images of cats and dogs that are labeled.
- Unlabeled Data: A large set of images that have not been labeled.
Process of Semi-Supervised Learning
- The model is first trained on a small set of labeled data (For example: images of cats and dogs).
- Then, it uses the patterns it learned to make predictions on the larger set of unlabeled data (Example: set of images with no label).
- These predictions are used to further refine the model. The model’s predictions on the unlabeled data help the model to improve.
- The model goes through several iterations of learning from both the labeled and the Step 3 predictions, refining its accuracy each time.
Benefits of Semi-Supervised Learning
- Saves time and resources since not all data needs labeling.
- Combines the strengths of both supervised and unsupervised learning for improved performance.
Applications of Semi-Supervised Learning
- Speech Recognition: Often uses semi-supervised learning to enhance accuracy with limited labeled audio data.
- Web Page Classification: Improves the categorization of web content using a small labeled
dataset and a large amount of unlabeled web pages.
In essence, semi-supervised learning strikes stability, leveraging both the structure from labeled data and the vast amounts of unlabeled data to create effective models.
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