Weak Supervision
Weak supervision is a branch of machine learning where noisy, limited, or imprecise sources are used to provide supervision signal for labeling large amounts of training data in a supervised learning setting. This approach alleviates the burden of obtaining hand-labeled data sets, which can be costly or impractical. Instead, inexpensive weak labels are employed with the understanding that they are imperfect, but can nonetheless be used to create a strong predictive model. Problem of labeled training data Machine learning models and techniques are increasingly accessible to researchers and developers; the real-world usefulness of these models, however, depends on access to high-quality labeled training data. This need for labeled training data often proves to be a significant obstacle to the application of machine learning models within an organization or industry. This bottleneck effect manifests itself in various ways, including the following examples: Insufficient quantity of l ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as trainin