Zobrazeno 1 - 10
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pro vyhledávání: '"del Jesus, María J."'
Photo-trapping cameras are widely employed for wildlife monitoring. Those cameras take photographs when motion is detected to capture images where animals appear. A significant portion of these images are empty - no wildlife appears in the image. Fil
Externí odkaz:
http://arxiv.org/abs/2312.14812
Autor:
Rivera, Antonio J., Dávila, Miguel A., Elizondo, David, del Jesus, María J., Charte, Francisco
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance.
Externí odkaz:
http://arxiv.org/abs/2305.17152
Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmet
Externí odkaz:
http://arxiv.org/abs/2301.06047
Publikováno v:
Neurocomputing 404 (2020) 93-107
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, c
Externí odkaz:
http://arxiv.org/abs/2005.10516
Publikováno v:
In: From Bioinspired Systems and Biomedical Applications to Machine Learning/IWINAC 2019. LNCS vol 11487. Springer (2019)
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulf
Externí odkaz:
http://arxiv.org/abs/2005.04321
High dimensionality, i.e. data having a large number of variables, tends to be a challenge for most machine learning tasks, including classification. A classifier usually builds a model representing how a set of inputs explain the outputs. The larger
Externí odkaz:
http://arxiv.org/abs/1802.08465
Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. T
Externí odkaz:
http://arxiv.org/abs/1802.05033
The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification. A handful of multilabel resampling methods have been proposed in late years, aiming to balance the label
Externí odkaz:
http://arxiv.org/abs/1802.05031
Autor:
Charte, Francisco, Rivera, Antonio J., Charte, David, del Jesus, María J., Herrera, Francisco
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, tra
Externí odkaz:
http://arxiv.org/abs/1802.03568
Publikováno v:
Information Fusion 44 (2018) 78-96
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as th
Externí odkaz:
http://arxiv.org/abs/1801.01586