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A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification
Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e. g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies.
The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems
Thus, the need for structured design knowledge for those systems arises.
Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data
An effective approach to reduce these costs is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specific querying individual data points (samples), which are then labeled (e. g., provided with class memberships) by a domain expert.
A New Vision of Collaborative Active Learning
Active learning (AL) is a learning paradigm where an active learner has to train a model (e. g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples.
