Machine Learning
Ranking with Predictive Clustering Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Gradient boosting for kernelized output spaces
Proceedings of the 24th international conference on Machine learning
Decision trees for hierarchical multi-label classification
Machine Learning
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
Content-based image retrieval by indexing random subwindows with randomized trees
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Incremental multi-target model trees for data streams
Proceedings of the 2011 ACM Symposium on Applied Computing
Tree ensembles for predicting structured outputs
Pattern Recognition
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We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled way. It inherits several features of these methods such as interpretability, robustness to irrelevant variables, and input scalability. When only the Gram matrix over the outputs of the learning sample is given, it learns the output kernel as a function of inputs. We show that the proposed algorithm works well on an image reconstruction task and on a biological network inference problem.