By Rachkovskii D.A., Slipchenko S.V.
Difficulties (inherent in compound hierarchical representations) of holding details at the constitution of coded facts and strategies of fixing them by way of "binding" of knowledge substructures are mentioned. one of those disbursed representations is taken into account within which facts are coded via multidimensional binary sparse vectors. the restrictions imposed on a binding process are formulated and its implementation is equipped. a few examples of illustration of based facts are thought of.
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Extra resources for A binding Procedure for Distributed Binary Data Representations
6’), but with the constraint removed. 3, the constraint k =1 k =1 N θ δ io (1) = δ io (1) ≥ ∑ λkδ ik (1), k =1 holds. But, since δ ik (1) = 1 for all k, then it is the case that N ∑λ k =1 k ≤ 1. On the output side, however, γ rk ( L) = 1 for all k, and any ordinal output r. But, since N ∑λ γ k =1 N it follows that k ∑ λk ≥ 1. Thus, k =1 rk ( L) ≥ γ ro ( L), N ∑λ k =1 k = 1. 4, it follows that the VRS and CRS models are equivalent. 6’) is equivalent to maximizing the sum of all slacks. 6) treats the Likert scale [1,L] as if it were a continuum, rather than as consisting of a set of discrete rank positions.
Because the presence of imprecise data invalidates the linear duality between the multiplier and envelopment DEA models. The multiplier IDEA (MIDEA), developed based upon the multiplier DEA model, presents the best efficiency scenario whereas the envelopment IDEA (EIDEA), developed based upon the envelopment DEA model, presents the worst efficiency scenario. Weight restrictions are often redundant if they are added into MIDEA. Alternative optimal solutions on the imprecise data can be determined using the recent sensitivity analysis approach.
M. M. Seiford. 1993. On the use of ordinal data in Data Envelopment Analysis. Journal of the Operational Research Society 44, 133-140. 34 Chapter 2 6. Cook, W. , M. M. Seiford. 1996. Data Envelopment Analysis in the presence of both quantitative and qualitative factors. Journal of the Operational Research Society 47, 945-953. 7. D. and J. Zhu. 2006. Rank order data in DEA : A general framework. European Journal of Operational Research 174, 1021-1038. 8. S. Park and G. Yu. 1999. IDEA and AR-IDEA: Models for dealing with imprecise data in DEA.