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Robust Classifiers without Robust Features
Neural Computation, 1990We develop a two-stage, modular neural network classifier and apply it to an automatic target recognition problem. The data are features extracted from infrared and TV images. We discuss the problem of robust classification in terms of a family of decision surfaces, the members of which are functions of a set of global variables.
Alan J. Katz +2 more
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Robustness and the robust estimate
Journal of Geodesy, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust optimization and portfolio selection: The cost of robustness
European Journal of Operational Research, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christine Gregory +2 more
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Robust Preferences and Robust Portfolio Choice
2009This chapter focuses on the problems of robust preferences and robust portfolio choice. The problem of portfolio choice consists in choosing, among all the available positions, a position that is affordable, given the investor's wealth w , and which is optimal with respect to the investor's preferences.
Föllmer, Hans +2 more
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Robust control with optimization of robustness index
2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012The paper considers a problem of robust control system synthesis based on the modified algorithm of Coefficient Diagram Method (CDM) with robustness index optimization. The proposed solution is expected to enable improvement of system robustness against parametric uncertainty.
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, 2011 The first example involves the real data given in Table 1 which are the results of an interlaboratory test. The boxplots are shown in Fig. 1 where the dotted line denotes the mean of the observations and the solid line the median. We note that only the results of the Laboratories 1 and 3 lie below the mean whereas all the remaining laboratories return ...
Gather, Ursula, Davies, P. Laurie
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Nature Reviews Genetics, 2004
Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems.
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Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems.
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Model robustness in robust identification
Proceedings of 1995 34th IEEE Conference on Decision and Control, 2002Considers the issue of model robustness. In particular the author considers fitting a linear transfer function model in the presence of a small deviation from linearity. The issue of deviation from linearity is often mentioned in robust identification but does not seem to have been explicitly treated before.
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2005
Intuitively, a complex network is robust if it keeps its basic functionality even under failure of some of its components. The study of robustness in networks is important because a thorough understanding of the behavior of certain classes of networks under failures and attacks may help to protect, for instance, communication networks like the Internet
Gunnar W. Klau, René Weiskircher
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Intuitively, a complex network is robust if it keeps its basic functionality even under failure of some of its components. The study of robustness in networks is important because a thorough understanding of the behavior of certain classes of networks under failures and attacks may help to protect, for instance, communication networks like the Internet
Gunnar W. Klau, René Weiskircher
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Robust Estimation and Robust Parameter
2020This chapter is addressed to the problem of defining the parameter in a semiparametric situation. Suppose, for example, that the observation X is assumed to be expressed as \(X=\theta +\varepsilon \), where \(\theta \) is the parameter to be estimated and \(\varepsilon \) is the error whose distribution is not specified by a finite number of parameters.
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