524 research outputs found
Linear and Order Statistics Combiners for Pattern Classification
Several researchers have experimentally shown that substantial improvements
can be obtained in difficult pattern recognition problems by combining or
integrating the outputs of multiple classifiers. This chapter provides an
analytical framework to quantify the improvements in classification results due
to combining. The results apply to both linear combiners and order statistics
combiners. We first show that to a first order approximation, the error rate
obtained over and above the Bayes error rate, is directly proportional to the
variance of the actual decision boundaries around the Bayes optimum boundary.
Combining classifiers in output space reduces this variance, and hence reduces
the "added" error. If N unbiased classifiers are combined by simple averaging,
the added error rate can be reduced by a factor of N if the individual errors
in approximating the decision boundaries are uncorrelated. Expressions are then
derived for linear combiners which are biased or correlated, and the effect of
output correlations on ensemble performance is quantified. For order statistics
based non-linear combiners, we derive expressions that indicate how much the
median, the maximum and in general the ith order statistic can improve
classifier performance. The analysis presented here facilitates the
understanding of the relationships among error rates, classifier boundary
distributions, and combining in output space. Experimental results on several
public domain data sets are provided to illustrate the benefits of combining
and to support the analytical results.Comment: 31 page
Thompson Sampling with Virtual Helping Agents
We address the problem of online sequential decision making, i.e., balancing
the trade-off between exploiting the current knowledge to maximize immediate
performance and exploring the new information to gain long-term benefits using
the multi-armed bandit framework. Thompson sampling is one of the heuristics
for choosing actions that address this exploration-exploitation dilemma. We
first propose a general framework that helps heuristically tune the exploration
versus exploitation trade-off in Thompson sampling using multiple samples from
the posterior distribution. Utilizing this framework, we propose two algorithms
for the multi-armed bandit problem and provide theoretical bounds on the
cumulative regret. Next, we demonstrate the empirical improvement in the
cumulative regret performance of the proposed algorithm over Thompson Sampling.
We also show the effectiveness of the proposed algorithm on real-world
datasets. Contrary to the existing methods, our framework provides a mechanism
to vary the amount of exploration/ exploitation based on the task at hand.
Towards this end, we extend our framework for two additional problems, i.e.,
best arm identification and time-sensitive learning in bandits and compare our
algorithm with existing methods.Comment: 14 pages, 8 figure
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Regression algorithms are regularly used for improving the accuracy of
satellite precipitation products. In this context, ground-based measurements
are the dependent variable and the satellite data are the predictor variables,
together with topography factors. Alongside this, it is increasingly recognised
in many fields that combinations of algorithms through ensemble learning can
lead to substantial predictive performance improvements. Still, a sufficient
number of ensemble learners for improving the accuracy of satellite
precipitation products and their large-scale comparison are currently missing
from the literature. In this work, we fill this specific gap by proposing 11
new ensemble learners in the field and by extensively comparing them for the
entire contiguous United States and for a 15-year period. We use monthly data
from the PERSIANN (Precipitation Estimation from Remotely Sensed Information
using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE
Retrievals for GPM) gridded datasets. We also use gauge-measured precipitation
data from the Global Historical Climatology Network monthly database, version 2
(GHCNm). The ensemble learners combine the predictions by six regression
algorithms (base learners), namely the multivariate adaptive regression splines
(MARS), multivariate adaptive polynomial splines (poly-MARS), random forests
(RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and
Bayesian regularized neural networks (BRNN), and each of them is based on a
different combiner. The combiners include the equal-weight combiner, the median
combiner, two best learners and seven variants of a sophisticated stacking
method. The latter stacks a regression algorithm on the top of the base
learners to combine their independent predictions...Comment: arXiv admin note: text overlap with arXiv:2301.0125
Interferometry of Aurigae: Characterization of the asymmetric eclipsing disk
We report on a total of 106 nights of optical interferometric observations of
the Aurigae system taken during the last 14 years by four beam
combiners at three different interferometric facilities. This long sequence of
data provides an ideal assessment of the system prior to, during, and after the
recent 2009-2011 eclipse. We have reconstructed model-independent images from
the 10 in-eclipse epochs which show that a disk-like object is indeed
responsible for the eclipse. Using new 3D, time-dependent modeling software, we
derive the properties of the F-star (diameter, limb darkening), determine
previously unknown orbital elements (, ), and access the global
structures of the optically thick portion of the eclipsing disk using both
geometric models and approximations of astrophysically relevant density
distributions. These models may be useful in future hydrodynamical modeling of
the system. Lastly, we address several outstanding research questions including
mid-eclipse brightening, possible shrinking of the F-type primary, and any
warps or sub-features within the disk.Comment: 105 pages, 57 figures. This is an author-created, un-copyedited
version of an article accepted for publication in Astrophysical Journal
Supplement Series. IOP Publishing Ltd is not responsible for any errors or
omissions in this version of the manuscript or any version derived from i
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