Semi-supervised learning is a machine learning technique that combines supervised and unsupervised learning to leverage datasets that contain few labelled examples and many unlabelled examples.
In semi-supervised learning, unsupervised learning algorithms are used to extract relevant features and useful representations from the unlabeled data, and then these insights are used to improve the quality of the supervised learning model. The supervised learning model is trained on both the labelled data and the unlabelled data, allowing information from the unlabelled data to be exploited to improve the accuracy of the model.
Semi-supervised learning is particularly useful in applications where the collection of labelled data is costly or difficult, but where a large amount of unlabelled data is available. Semi-supervised learning has been shown to significantly improve the accuracy of machine learning models in speech recognition, computer vision and natural language processing applications.
When seeking financing for companies, one of the most widely used formulas today is factoring. This is a resource that is not always [....]
Read More »Today we are going to explain the differences between a traditional CRM (Customer Relationship Management) and an intelligent CRM by applying technology that [...]
Read More »The commercial optimization software based on artificial intelligence must have feedback of the commercial actions carried out, of the nu [...]
Read More »The banking sector has undergone considerable transformations over the past 10 years. Especially as banking has become more integrated and [...]
Read More »
