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171.
The concept of super value nodes was established to allow dynamic programming to be performed within the theory of influence diagrams and to reduce the computational complexity in solving problems by means of influence diagrams. This paper is focused on how influence diagrams with super value nodes are affected by the presence of imprecise information. We analyze how to reduce the complexity when evaluating an influence diagram in this framework by modelling these kinds of nodes and random magnitudes in terms of fuzzy random variables. Finally, an applied example of the theoretical results is developed. 相似文献
172.
Implementation of new and innovative energy technologies is a key mean towards a sustainable energy system. Currently, governments have to decide from an increasingly diverse mix of them, the ones which warrant support, including funding and other incentives for private sector efforts. However, appraising energy technologies in terms of their sustainability is a really complex task, considering the series of uncertainties and implications that have to be encountered so as to obtain realistic and transparent results. In this context, the main aim of this paper is to present a direct and flexible multi-criteria decision making approach, using linguistic variables, to assist policy makers in formulating sustainable technological energy priorities. Furthermore, its software realization will be applied to a number of technologies, in the context of the Greek Technology Foresight Programme, and the results will be presented and discussed. 相似文献
173.
为了研究著名的Schneider投影问题,E.Lutwak,D.Yang和张高勇最近引进了一个关于对称多胞形的新的仿射不变量U(P),并提出了关于U(P)下界的猜想.本文就二维、三维在一特定条件下的情形给予了此猜想的肯定回答并给出了严格的数学证明. 相似文献
174.
Malek Adjouadi Nuannuan Zong Melvin Ayala 《Particle & Particle Systems Characterization》2005,22(2):107-118
This study introduces a new algorithm to optimize the pattern recognition of different white blood cell types in flow cytometry. The behavior of parametric data clusters in a multidimensional space is analyzed using the learning system known as Support Vector Machines (SVM). Beckman‐Coulter Corporation supplied flow cytometry data of numerous patients to be used as training and testing sets for the algorithm. Subsequently, the characteristics of the cells provided in these sets were used to train a SVM based classifier. The objective in developing this algorithm was to identify the category of a given blood sample and provide information to medical doctors in the form of diagnostic references for a specific disease state, lymphocytic leukemia. With the application of the hypothesis space, the learning bias and the learning algorithm, the SVM classifier was successfully trained to evaluate misclassification ratios in flow cytometry data in an effort to recognize abnormal blood cell patterns and address the ubiquitous problem of data overlap through the use of the maximal margin classifier. 相似文献
175.
This paper presents an error analysis for classification algorithms generated by regularization schemes with polynomial kernels.
Explicit convergence rates are provided for support vector machine (SVM) soft margin classifiers. The misclassification error
can be estimated by the sum of sample error and regularization error. The main difficulty for studying algorithms with polynomial
kernels is the regularization error which involves deeply the degrees of the kernel polynomials. Here we overcome this difficulty
by bounding the reproducing kernel Hilbert space norm of Durrmeyer operators, and estimating the rate of approximation by
Durrmeyer operators in a weighted L1 space (the weight is a probability distribution). Our study shows that the regularization parameter should decrease exponentially
fast with the sample size, which is a special feature of polynomial kernels.
Dedicated to Charlie Micchelli on the occasion of his 60th birthday
Mathematics subject classifications (2000) 68T05, 62J02.
Ding-Xuan Zhou: The first author is supported partially by the Research Grants Council of Hong Kong (Project No. CityU 103704). 相似文献
176.
支持向量机回归方法在地表水水质评价中的应用 总被引:2,自引:0,他引:2
马文涛 《数学的实践与认识》2007,37(9):45-50
将支持向量机方法应用于地表水质评价问题中,建立了多指标水质综合评价的支持向量机回归模型.在地表水质评价标准的基础上采用内插法获得学习样本,经过训练,得到水质评价的分类区间;然后以实测资料对所建模型进行检验,研究结果表明,支持向量机回归模型性能良好、预测精度高、简便易行,是水质评价的一种有效方法,具有广阔的应用前景. 相似文献
177.
Musielak-Orlicz空间是经典Orlicz空间的一种推广,ω^*光滑是Orlicz空间的一种重要几何性质,本讨论了Musielak-Orlicz空间中ω^*光滑点的充分必要条件。 相似文献
178.
179.
180.
We present an OR-based approach to support a milk collection problem in a special branch of dairy industry. The annual growth of the sector and the continuous imbalance between milk supply and demand, has urged the sector to look for a different approach to their daily milk collection problem. Specific details of the problem environment (i.e., the continuous production on supply level and the delivery conditions on demand level) gave rise to choose for a short- to medium-term planning approach. The proposed decision support system has to be considered as an efficient tool for generating stable milk collection plans which in turn also serves as an effective starting point for the vehicle routing problem. From a computational point of view it turned out that the application of Special Ordered Sets type 1 (SOS1) was very useful. Although it appears from literature that the computational advantage of SOS1 is restricted to supplementary model conditions, this study shows that these conditions are not necessarily needed. 相似文献