Difference between revisions of "Semi-Supervised"
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* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | ||
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | * [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | ||
| − | * [http://en.wikipedia.org/wiki/Semi-supervised_learning Semi-supervised] | + | * [http://en.wikipedia.org/wiki/Semi-supervised_learning Semi-supervised | Wikipedia] |
| + | * [[Learning Techniques]] | ||
As the name suggests, semi-supervised learning is a bit of both supervised and unsupervised learning and uses both labeled and unlabeled data for training. In a typical scenario, the algorithm would use a small amount of labeled data with a large amount of unlabeled data. This type of learning can again be used with methods such as classification, regression, and prediction. Examples of semi-supervised learning would be face and voice recognition techniques. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn] | As the name suggests, semi-supervised learning is a bit of both supervised and unsupervised learning and uses both labeled and unlabeled data for training. In a typical scenario, the algorithm would use a small amount of labeled data with a large amount of unlabeled data. This type of learning can again be used with methods such as classification, regression, and prediction. Examples of semi-supervised learning would be face and voice recognition techniques. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn] | ||
Revision as of 15:18, 8 December 2019
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- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
- Semi-supervised | Wikipedia
- Learning Techniques
As the name suggests, semi-supervised learning is a bit of both supervised and unsupervised learning and uses both labeled and unlabeled data for training. In a typical scenario, the algorithm would use a small amount of labeled data with a large amount of unlabeled data. This type of learning can again be used with methods such as classification, regression, and prediction. Examples of semi-supervised learning would be face and voice recognition techniques. Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn
