Add to ORKGAcquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency of dataset labelling. Using a simulated crowd-sourced label gathering scenario on a number of datasets, we show t...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Training machine learning models often requires large labelled datasets, which can be both expensive...
We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised ma...
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated t...
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated t...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are ...
Obtaining hand-labeled training data is one of the most tedious and expensive parts of the machine l...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are ...
Active learning methods have been proposed to reduce the labeling effort of human experts: based on ...
Supervised learning deals with the inference of a distribution over an output or label space conditi...
A constant challenge to researchers is the lack of large and timely datasets of domain examples (res...
We organized a data mining challenge on “active learning” for IJCNN/WCCI 2010, addressing machine le...
Obtaining labels can be expensive or time-consuming, but unlabeled data is often abundant and easier...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Training machine learning models often requires large labelled datasets, which can be both expensive...
We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised ma...
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated t...
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated t...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are ...
Obtaining hand-labeled training data is one of the most tedious and expensive parts of the machine l...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are ...
Active learning methods have been proposed to reduce the labeling effort of human experts: based on ...
Supervised learning deals with the inference of a distribution over an output or label space conditi...
A constant challenge to researchers is the lack of large and timely datasets of domain examples (res...
We organized a data mining challenge on “active learning” for IJCNN/WCCI 2010, addressing machine le...
Obtaining labels can be expensive or time-consuming, but unlabeled data is often abundant and easier...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Training machine learning models often requires large labelled datasets, which can be both expensive...
We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised ma...