Results 11 to 20 of about 1,335,864 (310)

Labelled splitting [PDF]

Annals of Mathematics and Artificial Intelligence, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fietzke, A., Weidenbach, C. ; https://orcid.org/0000-0001-6002-0458   +1 more
openaire   +5 more sources

Multi-Label Classification with Label Clusters

Knowledge and Information Systems, 2023
Abstract Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems.
Elaine Cecília Gatto, Mauri Ferrandin, Ricardo Cerri   +2 more
openaire   +2 more sources

Point labeling with sliding labels

Computational Geometry, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
van Kreveld, M.J., Strijk, T.W., Wolff, A.   +2 more
openaire   +2 more sources

Multi-label classification using ensembles of pruned sets [PDF]

, 2008
This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By
Pfahringer, Bernhard, Bernhard Pfahringer, Geoff Holmes, Holmes, Geoffrey, Jesse Read, Read, Jesse   +5 more
core   +1 more source

LABEL PROPAGATION FOR LEARNING WITH LABEL PROPORTIONS [PDF]

2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018
Accepted to MLSP ...
Rafael Poyiadzi, Raúl Santos-Rodríguez, Niall Twomey   +2 more
openaire   +5 more sources

A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection [PDF]

, 2015
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...
Suwimol Jungjit, Alex Freitas, Jungjit, Suwimol, Freitas, Alex A.   +3 more
core   +1 more source

Multi-label Learning with Label Enhancement [PDF]

2018 IEEE International Conference on Data Mining (ICDM), 2018
The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance.
Ruifeng Shao, Ning Xu 0009, Xin Geng 0001   +2 more
openaire   +3 more sources

Wasting the Future: The Technological Sublime, Communications Technologies, and E-waste

communication +1, 2012
Literally speaking, e-waste is the future of communications. E-waste is the fastest growing waste stream in the world, much of it communications technologies from cell phones to laptops, televisions to peripherals.
Sebine Label
doaj   +1 more source

Unveiling New States in 98Zr: Insights from β-decay and γ-γ Angular-Correlation Studies [PDF]

EPJ Web of Conferences
The nuclear structure of the 98Zr nucleus was studied through the β− decay of 98 Yg.s. at the TRIUMF-ISAC facility. The use of the 8π γ-ray spectrometer with its ancillary detectors SCEPTAR and PACES enabled γ-γ and γ-e− coincidence measurements as well ...
Mashtakov K. R., Garrett P. E., Olaizola B., Andreoiu C., Ball G. C., Bender P., Bildstein V., Chester A., Cross D. S., Dawkins H., Demand G. A., Diaz-Varela A., Deng G., Garnsworthy A. B., Hackman G., Hadinia B., Laffoley A. T., Moukaddam M., Park J., Peters E., Radich A. J., Rajabali M., Rand E. T., Rizwan U., Singh B., Starosta K., Svensson C. E., Voss P., Wang Z-M., Wood J. L., Yates S. W.   +30 more
doaj   +1 more source

Multi-label Classification with Meta-Labels [PDF]

2014 IEEE International Conference on Data Mining, 2014
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi ...
Jesse Read, Antti Puurula, Albert Bifet
openaire   +3 more sources