45,622 research outputs found
Estimator Selection: End-Performance Metric Aspects
Recently, a framework for application-oriented optimal experiment design has
been introduced. In this context, the distance of the estimated system from the
true one is measured in terms of a particular end-performance metric. This
treatment leads to superior unknown system estimates to classical experiment
designs based on usual pointwise functional distances of the estimated system
from the true one. The separation of the system estimator from the experiment
design is done within this new framework by choosing and fixing the estimation
method to either a maximum likelihood (ML) approach or a Bayesian estimator
such as the minimum mean square error (MMSE). Since the MMSE estimator delivers
a system estimate with lower mean square error (MSE) than the ML estimator for
finite-length experiments, it is usually considered the best choice in practice
in signal processing and control applications. Within the application-oriented
framework a related meaningful question is: Are there end-performance metrics
for which the ML estimator outperforms the MMSE when the experiment is
finite-length? In this paper, we affirmatively answer this question based on a
simple linear Gaussian regression example.Comment: arXiv admin note: substantial text overlap with arXiv:1303.428
API design for machine learning software: experiences from the scikit-learn project
Scikit-learn is an increasingly popular machine learning li- brary. Written
in Python, it is designed to be simple and efficient, accessible to
non-experts, and reusable in various contexts. In this paper, we present and
discuss our design choices for the application programming interface (API) of
the project. In particular, we describe the simple and elegant interface shared
by all learning and processing units in the library and then discuss its
advantages in terms of composition and reusability. The paper also comments on
implementation details specific to the Python ecosystem and analyzes obstacles
faced by users and developers of the library
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Kernel estimation techniques, such as mean shift, suffer from one major
drawback: the kernel bandwidth selection. The bandwidth can be fixed for all
the data set or can vary at each points. Automatic bandwidth selection becomes
a real challenge in case of multidimensional heterogeneous features. This paper
presents a solution to this problem. It is an extension of \cite{Comaniciu03a}
which was based on the fundamental property of normal distributions regarding
the bias of the normalized density gradient. The selection is done iteratively
for each type of features, by looking for the stability of local bandwidth
estimates across a predefined range of bandwidths. A pseudo balloon mean shift
filtering and partitioning are introduced. The validity of the method is
demonstrated in the context of color image segmentation based on a
5-dimensional space
Accuracy versus simplicity in online battery model identification
This paper presents a framework for battery
modeling in online, real-time applications where accuracy is
important but speed is the key. The framework allows users to
select model structures with the smallest number of parameters
that is consistent with the accuracy requirements of the target
application. The tradeoff between accuracy and speed in a battery
model identification process is explored using different model
structures and parameter-fitting algorithms. Pareto optimal sets
are obtained, allowing a designer to select an appropriate compromise
between accuracy and speed. In order to get a clearer
understanding of the battery model identification problem, “identification
surfaces” are presented. As an outcome of the battery
identification surfaces, a new analytical solution is derived for
battery model identification using a closed-form formula to obtain
a battery’s ohmic resistance and open circuit voltage from measurement
data. This analytical solution is used as a benchmark
for comparison of other fitting algorithms and it is also used in its
own right in a practical scenario for state-of-charge estimation.
A simulation study is performed to demonstrate the effectiveness
of the proposed framework and the simulation results are
verified by conducting experimental tests on a small NiMH
battery pack
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