Evolving Minimax Functions
Martha G Smons (Marthasimons)
on
March 9, 2021
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We propose a general method for estimating
the performance of a linear classifier, by
using a single, weighted, random sample-
based, linear ensemble estimator. Our
method has the following advantages: (1)
It is equivalent to a weighted Gaussian
process; (2) It is robust to any non-
linearity; and (3) It estimates the
expected probability of learning a given
class over the training set. We
demonstrate this by using a variety of
experiments where the expected probability
of learning a given class over the
training set is highly predictive, and the
prediction error depends on the degree of
belief of the classifier, which differs
between the predictions obtained by the
estimator and the estimators themselves.
We illustrate several such scenarios in
one graphical model.
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---------A Multi-Camera System Approach
for Real-time 6DOF Camera Localization
This paper presents an approach for 3D
camera tracking using a real-world multi-
camera system. Existing approaches to 3D
camera tracking have been built on the
ground-truth in which a 3D camera system
consists of a three-dimensional camera
system and a real-time 3D camera system.
Due to the physical layout of the system
and the appearance of the environment, the
3D camera system needs to be able to
capture the 3D environment. The system
comprises of a computer-based 2D camera
system and a 3D camera system that can be
projected onto a real-world 3D camera
system. The computer-based 2D camera
system and the real-world 3D camera system
are integrated into one system. A novel
approach to 3D camera tracking has been
designed for solving this problem. A
large-scale dataset of real-world 3D
cameras was collected and compared to two
baseline tracking algorithms. Experimental
evaluation on both datasets shows that a
high accuracy tracking and tracking
algorithms are able to obtain the best
results with respect to a baseline
algorithm which was developed for 3D
camera tracking.
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