5,199 research outputs found
Boosted Random ferns for object detection
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class variability. Second, both the positions where ferns are evaluated within the sliding window, and the location of the binary features for each fern are not chosen completely at random, but instead we use a boosting strategy to pick the most discriminative combination of them. This is further enhanced by our third contribution, that is to adapt the boosting strategy to enable sharing of binary features among different ferns, yielding high recognition rates at a low computational cost. And finally, we show that training can be performed online, for sequentially arriving images. Overall, the resulting classifier can be very efficiently trained, densely evaluated for all image locations in about 0.1 seconds, and provides detection rates similar to competing approaches that require expensive and significantly slower processing times. We demonstrate the effectiveness of our approach by thorough experimentation in publicly available datasets in which we compare against state-of-the-art, and for tasks of both 2D detection and 3D multi-view estimation.Peer ReviewedPostprint (author's final draft
TreeGrad: Transferring Tree Ensembles to Neural Networks
Gradient Boosting Decision Tree (GBDT) are popular machine learning
algorithms with implementations such as LightGBM and in popular machine
learning toolkits like Scikit-Learn. Many implementations can only produce
trees in an offline manner and in a greedy manner. We explore ways to convert
existing GBDT implementations to known neural network architectures with
minimal performance loss in order to allow decision splits to be updated in an
online manner and provide extensions to allow splits points to be altered as a
neural architecture search problem. We provide learning bounds for our neural
network.Comment: Technical Report on Implementation of Deep Neural Decision Forests
Algorithm. To accompany implementation here:
https://github.com/chappers/TreeGrad. Update: Please cite as: Siu, C. (2019).
"Transferring Tree Ensembles to Neural Networks". International Conference on
Neural Information Processing. Springer, 2019. arXiv admin note: text overlap
with arXiv:1909.1179
Finding Black Holes with Black Boxes -- Using Machine Learning to Identify Globular Clusters with Black Hole Subsystems
Machine learning is a powerful technique, becoming increasingly popular in
astrophysics. In this paper, we apply machine learning to more than a thousand
globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I'
project in order to correlate present-day observable properties with the
presence of a subsystem of stellar mass black holes (BHs). The machine learning
model is then applied to available observed parameters for Galactic GCs to
identify which of them that are most likely to be hosting a sizeable number of
BHs and reveal insights into what properties lead to the formation of BH
subsystems. With our machine learning model, we were able to shortlist 21
Galactic GCs that are most likely to contain a BH subsystem. We show that the
clusters shortlisted by the machine learning classifier include those in which
BH candidates have been observed (M22, M10 and NGC 3201) and that our results
line up well with independent simulations and previous studies that manually
compared simulated GC models with observed properties of Galactic GCs. These
results can be useful for observers searching for elusive stellar mass BH
candidates in GCs and further our understanding of the role BHs play in GC
evolution. In addition, we have released an online tool that allows one to get
predictions from our model after they input observable properties.Comment: 20 pages, 9 figures, 7 tables. Accepted for publication in MNRAS.
Source code available at
https://github.com/ammaraskar/black-holes-black-boxe
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Building more accurate decision trees with the additive tree.
The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
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