Now showing items 1-5 of 5
Aggregated outputs by linear models: An application on marine litter beaching prediction
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 ...
Crowd Learning with Candidate Labeling: an EM-based Solution
Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ...
A note on the behavior of majority voting in multi-class domains with biased annotators
Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...
Learning to classify software defects from crowds: a novel approach
In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...
Fitting the data from embryo implantation prediction: Learning from label proportions
Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve ...