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Found 4 papers, 2 papers with code
Probabilistic Active Learning for Active Class Selection
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's performance while minimizing the number of requests.
Active Selection of Classification Features
Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task's performance most when acquiring their expensive features z and including them to the primary training set.
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications.
Temporal Density Extrapolation using a Dynamic Basis Approach
Density estimation is a versatile technique underlying many data mining tasks and techniques, ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data.
