## Search

Now showing items 1-10 of 10

#### Optimization of deep learning precipitation models using categorical binary metrics

(2020)

This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...

#### Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

(2019-09-01)

In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ...

#### Aggregated outputs by linear models: An application on marine litter beaching prediction

(2019-01-01)

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 ...

#### A note on the behavior of majority voting in multi-class domains with biased annotators

(2018-05)

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

(2017-11-01)

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 ...

#### A system for airport weather forecasting based on circular regression trees

(2017-11-01)

This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously ...

#### Measuring the Class-imbalance Extent of Multi-class Problems

(2017-07-30)

Since many important real-world classification problems involve learning from unbalanced data, the challenging class-imbalance problem has lately received con- siderable attention in the community. Most of the methodological ...

#### Efficient approximation of probability distributions with k-order decomposable models

(2016-07)

During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...

#### Efficient approximation of probability distributions with k-order decomposable models

(2016-01-01)

During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...

#### Fitting the data from embryo implantation prediction: Learning from label proportions

(2016-01-01)

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 ...