The results from L1-Endmembers display the algorithm's stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the SPICE algorithm an...
详细信息
ISBN:
(纸本)9781424495665
The results from L1-Endmembers display the algorithm's stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the SPICE algorithm and the Virtual Dimensionality methods for estimating the number of endmembers. Furthermore, the results shown for this algorithm were generated with the same parameter set for all of the data sets, from two-dimensional data to 51-dimensional real hyperspectral data. This indicates L1-Endmembers may lack of sensitivity to parameter value settings. The L1-Endmembers algorithm requires several quadratic programming steps per iteration. These can be completed directly in quadratic programming software packages such as CPLEX and take advantage of any running time reductions the software packages provide. Investigations will be conducted into whether the specific form of this algorithm, particularly with respect to the constraints on the abundance values, can be used to reduce the running time.
In the present paper we develop our approach for studying the stability of integer programming problems. We prove that the L-class enumeration method is stable on integer linear programming problems in the case of bou...
详细信息
In the present paper we develop our approach for studying the stability of integer programming problems. We prove that the L-class enumeration method is stable on integer linear programming problems in the case of bounded relaxation sets. The stability of some cutting plane algorithms is discussed. (c) 2005 Elsevier B.V. All rights reserved.
Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundarie...
详细信息
Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundaries/points change with small perturbations to the training data due to algorithm instability. Moreover, rule induction typically produces a large number of rules that must be filtered and interpreted by an analyst. This paper describes a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst, to measure and increase the stability of the rule induction process, and to provide a measure of variance to continuous attribute decision boundaries and accuracy point estimates. A measure of similarity between rules is also introduced as a basis of multidimensional scaling to visualize rule similarity. The method was applied to perioperative data and to the UCI ( University of California, Irvine) thyroid dataset.
This paper describes a method for high speed input (and state) estimation in continuous state space models, given only discretely sampled output data. The state-space models are considered to describe either single-in...
详细信息
ISBN:
(纸本)0852965095
This paper describes a method for high speed input (and state) estimation in continuous state space models, given only discretely sampled output data. The state-space models are considered to describe either single-input/single-output, or more complex multi-input/multi-output (MIMO) sensors operating in continuous time. A full derivation of the algorithms is given, and the performance of such algorithms is discussed in terms of algorithm stability and speed of convergence under initialisation error. It is finally shown how the convergence of the algorithms may be accelerated by considering a slight variation on the conventional state-space equations.
暂无评论