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11.
In this paper we investigate data structures obtained by a recursive partitioning of the multi- dimensional input domain
into regions of equal size. One of the best known examples of such a structure is the quadtree . It is used here as a basis for more complex data structures. We also provide multidimensional versions of the stratified tree by van Emde Boas [vEB]. We show that under the assumption that the input points have limited precision (i.e., are drawn
from the integer grid of size u ) these data structures yield efficient solutions to many important problems. In particular, they allow us to achieve O(log log u) time per operation for dynamic approximate nearest neighbor (under insertions and deletions) and exact on-line closest pair
(under insertions only) in any constant number of dimensions. They allow O(log log u) point location in a given planar shape or in its expansion (dilation by a ball of a given radius). Finally, we provide a
linear time (optimal) algorithm for computing the expansion of a shape represented by a region quadtree. This result shows
that the spatial order imposed by this regular data structure is sufficient to optimize the operation of dilation by a ball.
Received January 19, 1999; revised November 4, 1999. 相似文献
12.
Taghi M. Khoshgoftaar Xiaojing Yuan Edward B. Allen 《Empirical Software Engineering》2000,5(4):313-330
Software product and process metrics can be useful predictorsof which modules are likely to have faults during operations.Developers and managers can use such predictions by softwarequality models to focus enhancement efforts before release.However, in practice, software quality modeling methods in theliterature may not produce a useful balance between the two kindsof misclassification rates, especially when there are few faultymodules.This paper presents a practical classificationrule in the context of classification tree models that allowsappropriate emphasis on each type of misclassification accordingto the needs of the project. This is especially important whenthe faulty modules are rare.An industrial case study using classification trees, illustrates the tradeoffs.The trees were built using the TREEDISC algorithm whichis a refinement of the CHAID algorithm. We examinedtwo releases of a very large telecommunications system, and builtmodels suited to two points in the development life cycle: theend of coding and the end of beta testing. Both trees had onlyfive significant predictors, out of 28 and 42 candidates, respectively.We interpreted the structure of the classification trees, andwe found the models had useful accuracy. 相似文献
13.
In this paper, field experimental comparison is made between a small track-type experimental prototype skidder and a J-50 skidding tractor. Experimental data, including skidding productivity, soil compaction on skidding trails, and damage rate of the residual trees, are analyzed. The results indicate that with the condition of scattered skidding area and low skidding volume per cycle, small track-type experimental prototype skidder has advantage on working and a higher skidding productivity. It makes lower soil compaction to the skidding trails in the depth of 0-5 cm, 5-10 cm, and 10-15 cm. Under the same work conditions, the damage rate of the residual trees made by small track-type experimental prototype skidder is only 1/5 of those made by J-50 type skidding tractor. The damage rate is reduced by 80%. 相似文献
14.
Fitness landscapes have proved to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. Usually, a fitness landscape is considered as a mapping from a configuration space equipped with some notion of adjacency, nearness, distance, or accessibility, into the real numbers. In the context of multi-objective optimization problems this concept can be extended to poset-valued landscapes. In a geometric analysis of such a structure, local Pareto points take on the role of local minima. We show that the notion of saddle points, barriers, and basins can be extended to the poset-valued case in a meaningful way and describe an algorithm that efficiently extracts these features from an exhaustive enumeration of a given generalized landscape. 相似文献
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Yonghong Peng 《Journal of Intelligent Manufacturing》2004,15(3):373-380
Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques. 相似文献
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Luis Enrique Sucar Joaquín Pérez-Brito J. Carlos Ruiz-Suárez Eduardo Morales 《Applied Intelligence》1997,7(4):327-338
In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters. 相似文献
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