International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Machine Learning
Machine Learning
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A general framework for accurate and fast regression by data summarization in random decision trees
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Additive Groves of Regression Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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We present and investigate ensembles of semi-random model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivalling the state of the art in numeric prediction. An empirical investigation shows that Semi-Random Model Trees produce predictive performance which is competitive with state-of-the-art methods like Gaussian Processes Regression or Additive Groves of Regression Trees. The training and optimization of Random Model Trees scales better than Gaussian Processes Regression to larger datasets, and enjoys a constant advantage over Additive Groves of the order of one to two orders of magnitude.