Active learning is a machine learning technique in which a machine learning model asks a user to manually label a small selection of training data to improve its performance. Instead of waiting for a large set of labelled data to be available for training, the active learning model uses a sample selection strategy to choose which data to request for labelling.
Sample selection is based on the model's degree of uncertainty about a sample, meaning that the model chooses samples that it believes are more difficult to classify. After the user labels these samples, the model is trained on the updated dataset and repeats the process.
Active learning is particularly useful in situations where manual labelling of data may be costly or difficult to obtain. For example, in medical image classification, it can be difficult to obtain large amounts of labelled data, but active learning can help improve model accuracy with careful selection of samples for labelling.
What is Digital Transformation? The industrial revolution profoundly changed the society of the 19th century, but the digital transformation of the [...]
Read More »You are probably wondering, what is surety insurance and how does it help your company? In today's economic environment, [...]
Read More »More and more companies are taking advantage of the relevant information they extract from the data they possess and generate to improve their processes and discover new ways to [...]
Read More »Intelligent Process Automation in companies has changed in the world very rapidly in recent years. The COVID-19, the interr [...]
Read More »
