29,241 research outputs found
Growing Regression Forests by Classification: Applications to Object Pose Estimation
In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first finds clusters of the training data which at least locally minimize the
empirical loss without considering the input space. Then splitting rules which
preserve the found clusters as much as possible are determined by casting the
problem into a classification problem. Consequently, our new node splitting
method enjoys more freedom in choosing the splitting rules, resulting in more
efficient tree structures. In addition to the Euclidean target space, we
present a variant which can naturally deal with a circular target space by the
proper use of circular statistics. We apply the regression forest employing our
node splitting to head pose estimation (Euclidean target space) and car
direction estimation (circular target space) and demonstrate that the proposed
method significantly outperforms state-of-the-art methods (38.5% and 22.5%
error reduction respectively).Comment: Paper accepted by ECCV 201
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger. The software is a fast
implementation of random forests for high dimensional data. Ensembles of
classification, regression and survival trees are supported. We describe the
implementation, provide examples, validate the package with a reference
implementation, and compare runtime and memory usage with other
implementations. The new software proves to scale best with the number of
features, samples, trees, and features tried for splitting. Finally, we show
that ranger is the fastest and most memory efficient implementation of random
forests to analyze data on the scale of a genome-wide association study
Random forests with random projections of the output space for high dimensional multi-label classification
We adapt the idea of random projections applied to the output space, so as to
enhance tree-based ensemble methods in the context of multi-label
classification. We show how learning time complexity can be reduced without
affecting computational complexity and accuracy of predictions. We also show
that random output space projections may be used in order to reach different
bias-variance tradeoffs, over a broad panel of benchmark problems, and that
this may lead to improved accuracy while reducing significantly the
computational burden of the learning stage
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