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Symbolic feedback for transparent fault anticipation in neuroergonomic brain-machine interfaces. [PDF]
Front Robot AIMahrouk A.
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Symbolic kernel discriminant analysis
Computational Statistics, 1998Kernel density estimation is a tool which allows the statistician to construct a density on any sample of data without any prior probabilistic hypothesis. These methods compute a weighted sum of kernels centered on each data point. Examples of kernel density estimations are to be found essentially for quantitative (discrete or continuous) and ...
Rasson, Jean-Paul, Lissoir, Sandrine
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International Journal of Signs and Semiotic Systems, 2014
Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of
Sandra Elizabeth González Císaro +1 more
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Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of
Sandra Elizabeth González Císaro +1 more
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IEE Proceedings G (Electronic Circuits and Systems), 1981
A new approach to the perturbation theory of linear systems is developed and is applied to the computerised generation of symbolic functions. The function is defined in terms of increments to, rather than, the element values themselves. The modification leads to considerable theoretical and computational simplifications.
K. Singhal, J. Vlach
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A new approach to the perturbation theory of linear systems is developed and is applied to the computerised generation of symbolic functions. The function is defined in terms of increments to, rather than, the element values themselves. The modification leads to considerable theoretical and computational simplifications.
K. Singhal, J. Vlach
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2009 30th IEEE Real-Time Systems Symposium, 2009
A key feature of control systems is robustness, the property that small perturbations in the system inputs cause only small changes in its outputs. Robustness is key to designing systems that work under uncertain or imprecise environments. While continuous control design algorithms can explicitly incorporate robustness as a design goal, it is not clear
Rupak Majumdar, Indranil Saha
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A key feature of control systems is robustness, the property that small perturbations in the system inputs cause only small changes in its outputs. Robustness is key to designing systems that work under uncertain or imprecise environments. While continuous control design algorithms can explicitly incorporate robustness as a design goal, it is not clear
Rupak Majumdar, Indranil Saha
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Symbolic Objects and Symbolic Data Analysis
2005Today’s technology allows storing vast quantities of information from different sources in nature. This information has missing values, nulls, internal variation, taxonomies, and rules. We need a new type of data that allow us to represent the complexity of reality, maintaining the internal variation and structure (Bock & Diday, 2000; Diday, 2002 ...
Héctor Oscar Nigro +1 more
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2002
Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic interval-valued data. The present paper compares that approach with several possible alternative models using classical techniques; the symbolic regression approach is preferred. Thence, a regression approach is provided for symbolic histogram-valued data.
Lynne Billard, Edwin Diday
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Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic interval-valued data. The present paper compares that approach with several possible alternative models using classical techniques; the symbolic regression approach is preferred. Thence, a regression approach is provided for symbolic histogram-valued data.
Lynne Billard, Edwin Diday
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1991
In the previous chapter, the algorithmic aspects of the symbolic simulation of analog circuits have been presented. The basic concepts have been illustrated for the symbolic simulator ISAAC [GIE_89c]. All analyses, however, were restricted to AC characteristics of linear(ized) circuits.
Georges Gielen, Willy Sansen
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In the previous chapter, the algorithmic aspects of the symbolic simulation of analog circuits have been presented. The basic concepts have been illustrated for the symbolic simulator ISAAC [GIE_89c]. All analyses, however, were restricted to AC characteristics of linear(ized) circuits.
Georges Gielen, Willy Sansen
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