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The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where 0<q?1. This problem is equivalent to finding the strip defined by two parallel lines of minimum vertical separation that encloses at least half of the points.The best known exact algorithm for this problem runs in O(n2) time. We consider two types of approximations, a residual approximation, which approximates the vertical height of the strip to within a given error bound εr?0, and a quantile approximation, which approximates the fraction of points that lie within the strip to within a given error bound εq?0. We present two randomized approximation algorithms for the LMS line estimator. The first is a conceptually simple quantile approximation algorithm, which given fixed q and εq>0 runs in O(nlogn) time. The second is a practical algorithm, which can solve both types of approximation problems or be used as an exact algorithm. We prove that when used as a quantile approximation, this algorithm's expected running time is . We present empirical evidence that the latter algorithm is quite efficient for a wide variety of input distributions, even when used as an exact algorithm.  相似文献   
2.
Algorithms are developed that determine canonic forms and grammar categories from individual words and the vocabulary of a text. Algorithms are proved to be highly efficient. The accuracy of determination directly depends on the length of the text.  相似文献   
3.
We examine the complexity of testing different program constructs. We do this by defining a measure of testing complexity known as VCP-dimension, which is similar to the Vapnik–Chervonenkis dimension, and applying it to classes of programs, where all programs in a class share the same syntactic structure. VCP-dimension gives bounds on the number of test points needed to determine that a program is approximately correct, so by studying it for a class of programs we gain insight into the difficulty of testing the program construct represented by the class. We investigate the VCP-dimension of straight line code, if–then–else statements, and for loops. We also compare the VCP-dimension of nested and sequential if–then–else statements as well as that of two types of for loops with embedded if–then–else statements. Finally, we perform an empirical study to estimate the expected complexity of straight line code.  相似文献   
4.
A 25-yr-old woman with incapacitating hypochondriasis (disease phobia and conviction of being infected with herpes genitalis) for 4 yrs and agoraphobia with panic attacks for 2 yrs was well after 6 mo of treatment and at 14-mo follow-up. Agoraphobia was treated with a combination of desensitization in imagery, aided by relaxation training using electromyogram (EMG) and skin temperature biofeedback, followed by exposure in vivo. Hypochondriasis was treated by psychotherapeutic techniques and presentation of medical facts. (20 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)  相似文献   
5.
Abstract

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6.
The problem of localization, that is, of a robot finding its position on a map, is an important task for autonomous mobile robots. It has applications in numerous areas of robotics ranging from aerial photography to autonomous vehicle exploration. In this paper we present a new strategy LPS (Localize-by-Placement-Separation) for a robot to find its position on a map, where the map is represented as a geometric tree of bounded degree. Our strategy exploits to a high degree the self-similarities that may occur in the environment. We use the framework of competitive analysis to analyze the performance of our strategy. In particular, we show that the distance traveled by the robot is at most O( ) times longer than the shortest possible route to localize the robot, where n is the number of vertices of the tree. This is a significant improvement over the best known previous bound of O(n2/3). Moreover, since there is a lower bound of Ω( ), our strategy is optimal up to a constant factor. Using the same approach we can also show that the problem of searching for a target in a geometric tree, where the robot is given a map of the tree and the location of the target but does not know its own position, can be solved by a strategy with a competitive ratio of O( ), which is again optimal up to a constant factor.  相似文献   
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