corel16k009

mldr.datasets::get.mldr("corel16k009")

Select your download

Partitions: select your desired partitioning strategy, validation and format

Random Stratified Iterative stratified
Hold out MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
10-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr

Summary

Instances 13884
Attributes 673
Inputs 500
Labels 173
Labelsets 5175
Single labelsets 3346
Max frequency 177
Cardinality 2.9301
Density 0.0169
Mean IR 36.4456
SCUMBLE 0.2978
TCS 19.9195

Citation

Barnard, K.; Duygulu, P.; Forsyth, D.; de Freitas, N.; Blei, D. M.; Jordan, M. I. (2003). Matching words and pictures. In Journal of Machine Learning Research, 3(), 1107--1135.
@article{,
    title = "Matching words and pictures",
    author = "Barnard, K. and Duygulu, P. and Forsyth, D. and de Freitas, N. and Blei, D. M. and Jordan, M. I.",
    journal = "Journal of Machine Learning Research",
    year = "2003",
    volume = "3",
    pages = "1107--1135",
}

Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.