30,419 research outputs found
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
The efficacy of particle identification is compared using artificial neutral
networks and boosted decision trees. The comparison is performed in the context
of the MiniBooNE, an experiment at Fermilab searching for neutrino
oscillations. Based on studies of Monte Carlo samples of simulated data,
particle identification with boosting algorithms has better performance than
that with artificial neural networks for the MiniBooNE experiment. Although the
tests in this paper were for one experiment, it is expected that boosting
algorithms will find wide application in physics.Comment: 6 pages, 5 figures; Accepted for publication in Nucl. Inst. & Meth.
Cascade Training Technique for Particle Identification
The cascade training technique which was developed during our work on the
MiniBooNE particle identification has been found to be a very efficient way to
improve the selection performance, especially when very low background
contamination levels are desired. The detailed description of this technique is
presented here based on the MiniBooNE detector Monte Carlo simulations, using
both artifical neural networks and boosted decision trees as examples.Comment: 12 pages and 4 EPS figure
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