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.
In the digital age in which we live, artificial intelligence (AI) has emerged as a disruptive force in numerous industries, and the banking sector has been [...]
Read More »Chargeback refers to refunds that occur when, at the request of a cardholder, the bank requests a refund on his or her behalf [...].
Read More »In today's oversaturated information market, it is becoming increasingly difficult to retain users. For companies, competition is increasingly [...]
Read More »In the previous articles ("Basic concepts to build a commercial software with artificial intelligence" and "How to materialize the opportun [...]
Read More »