Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks

2014-12-01 · NeurIPS 2014 12 · Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor

We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable inference. The experimental results confirm that practical learning is scalable to realistic datasets using this approach.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Multilabel Classification through Random Graph Ensembles

2013-10-31 · Hongyu Su, Juho Rousu

We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured output learner as the base classifier. Fo…

Classification Diversity Ensemble Learning General Classification

Gradient Boosted Decision Trees for High Dimensional Sparse Output

2017-08-01 · ICML 2017 8 · Si Si, huan zhang, S. Sathiya Keerthi, Dhruv Mahajan 외

In this paper, we study the gradient boosted decision trees (GBDT) when the output space is high dimensional and sparse. For example, in multilabel classification, the output space is a $L$-dimensional 0/1 vector, w…

General Classification Vocal Bursts Intensity Prediction

Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification

2012-12-01 · NeurIPS 2012 12 · Wei Bi, James T. Kwok

In hierarchical classification, the prediction paths may be required to always end at leaf nodes. This is called mandatory leaf node prediction (MLNP) and is particularly useful when the leaf nodes have much stronger sem…

Classification General Classification Prediction

Scalable Multilabel Prediction via Randomized Methods

2015-02-09 · Nikos Karampatziakis, Paul Mineiro

Modeling the dependence between outputs is a fundamental challenge in multilabel classification. In this work we show that a generic regularized nonlinearity mapping independent predictions to joint predictions is suffic…

General Classification Prediction

On the Generalization of the C-Bound to Structured Output Ensemble Methods

2014-08-06 · François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy

This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the \Cbound, an upper bound over…

Binary Classification General Classification