Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data.A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate known labels through the edges of the ...
If two points x1 and x2 are located in a local neighborhood in the low-dimensional manifold, they have similar class labels [28]. This assumption reflects the local smoothness of the decision boundary.It is well known that one of the problems of machine learning algorithms is the curse of ...
In research, data sets used for evaluating semi-supervised learning algorithms are usually obtained by simply removing the labels of a large amount of data points from an existing supervised learning data set. In practice, the choice of data sets and their partitioning can have significant impact ...
Making Systems Fail-Aware: A Semi-Supervised Machine Learning Approach for Identifying Failures by Learning the Correct Behavior of a Systemmonitoringfault detectionapplication of machine learningObserving the interaction between a system, its environment, and its internal state is vital to detect failures...
Accordantly, while a 2018 study of semi-supervised learning algorithms found that “increasing the amount of unlabeled data tends to improve the performance of SSL techniques,” it also found that “adding unlabeled data from a mismatched set of classes can actuallyhurtperformance compared to not us...
Our semi-supervised machine-learning algorithms benefit from high-classification performance while being trained on data sets small enough to be manually annotated by individual experts. Although this work has been a case study specifically for classifying materials synthesis paragraphs, the applicability of...
30 Semi-Supervised Learning Algorithms. Contribute to YGZWQZD/LAMDA-SSL development by creating an account on GitHub.
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training exampl
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. [pdf] [code] Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow. NeurIPS 2018 Semi-Supervised Learning Literature Survey. [pdf] Xiaojin Zhu. 2008 An Overview of Deep Semi-Supervised Learning. [pdf]...
The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. ...