Aggregated outputs by linear models: An application on marine litter beaching prediction. Hernández-González, J., Inza, I., Granado, I., Basurko, O., C., Fernandes, J., A., & Lozano, J., A. Information Sciences, 481:381-393, 2019.
abstract   bibtex   
In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning with aggregated outputs, the examples available for model training are individually unlabeled. Collectively, the aggregated outputs of different subsets of training examples are provided. In this paper, we propose an iterative methodology to learn linear models from this type of data. In spite of being simple, its competitive performance is shown in comparison with a straightforward solution and state-of-the-art techniques. A real world problem is also illustrated which naturally fits the aggregated outputs framework: the estimation of marine litter beaching along the south-east coastline of the Bay of Biscay.
@article{
 title = {Aggregated outputs by linear models: An application on marine litter beaching prediction},
 type = {article},
 year = {2019},
 keywords = {Aggregated outputs,Expectation–Maximization,Linear models,Machine learning,Marine litter beaching,Regression},
 pages = {381-393},
 volume = {481},
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 created = {2021-11-12T08:30:57.939Z},
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 last_modified = {2021-11-12T08:30:57.939Z},
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 abstract = {In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning with aggregated outputs, the examples available for model training are individually unlabeled. Collectively, the aggregated outputs of different subsets of training examples are provided. In this paper, we propose an iterative methodology to learn linear models from this type of data. In spite of being simple, its competitive performance is shown in comparison with a straightforward solution and state-of-the-art techniques. A real world problem is also illustrated which naturally fits the aggregated outputs framework: the estimation of marine litter beaching along the south-east coastline of the Bay of Biscay.},
 bibtype = {article},
 author = {Hernández-González, Jerónimo and Inza, Iñaki and Granado, Igor and Basurko, Oihane C and Fernandes, Jose A and Lozano, Jose A},
 journal = {Information Sciences}
}

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