Guest editorial special issue on structured multi-output learning: modeling, algorithm, theory, and applications
Abstract
Structured multioutput learning is a topic in artificial intelligence that considers multiple structured outputs prediction for a given input. The output may involve structured objects in the form of sequence, string, tree, lattice, or graph and has values that are characterized by diverse data types, such as binary, nominal, ordinal, and real-valued variables. Such learning problems arise in a variety of real-world applications, ranging from document classification, computer emulation, sensor network analysis, concept-based information retrieval, and human action/causal induction to video analysis, image annotation/retrieval, gene function prediction, and brain science. As many complex real-world scenarios can be posed as a structured multioutput learning problem, their importance and popularity have been increasing steadily.
Authors: Weiwei Liu, Xiaobo Shen, Yew-Soon Ong, Ivor Wai-Hung Tsang, Chen Gong, Vladimir Pavlović
Published in: IEEE Transactions on Neural Networks and Learning Systems (2020)