What is semi-supervised feature selection?
Semi-supervised filter feature selection methods examine intrinsic properties of the labeled and unlabeled data to evaluate the features prior to the learning tasks. Most of the semi-supervised filter feature selection methods correspond to graph-based semi-supervised feature selection methods from the second taxonomy.
What is the difference between supervised and semi-supervised learning?
In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. Semi-supervised learning takes a middle ground. It uses a small amount of labeled data bolstering a larger set of unlabeled data.
What are the applications of supervised learning?
There are some very practical applications of supervised learning algorithms in real life, including:
- Text categorization.
- Face Detection.
- Signature recognition.
- Customer discovery.
- Spam detection.
- Weather forecasting.
- Predicting housing prices based on the prevailing market price.
- Stock price predictions, among others.
Which is better semi supervised or supervised feature learning?
Semi-supervised feature learning significantly outperforms supervised feature learning when annotated data are limited in the training set, e.g., weakly labeled or unlabeled data are available , .
How is semi supervised feature learning for improving writer identification?
In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.
Which is a wrapper method for semi-supervised learning?
Self-training is a wrapper method for semi-supervised learning. First a supervised learning algorithm is trained based on the labeled data only. This classifier is then applied to the unlabeled data to generate more labeled examples as input for the supervised learning algorithm.
What is the smoothness assumption in semi supervised learning?
This is also generally assumed in supervised learning and yields a preference for geometrically simple decision boundaries. In the case of semi-supervised learning, the smoothness assumption additionally yields a preference for decision boundaries in low-density regions, so few points are close to each other but in different classes.