Difference between revisions of "Semi-Supervised"
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* [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | * [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | ||
| − | 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 | 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] |
http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_4.jpg | http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_4.jpg | ||
Revision as of 15:06, 3 June 2018
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
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
