84 citations · 134 across the 3 of their papers we have counts for
3 papers
cs.LG2015★ 29 cited
Deep Learning for Multi-label Classification
Jesse Read, Fernando Perez-Cruz
In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time.…
stat.ML2015★ 84 cited
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
J. Read, L. Martino, P. Olmos +1
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier…
cs.AI2014★ 21 cited
Kaggle LSHTC4 Winning Solution
Antti Puurula, Jesse Read, Albert Bifet
Our winning submission to the 2014 Kaggle competition for Large Scale Hierarchical Text Classification (LSHTC) consists mostly of an ensemble of sparse generative models extending…