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...we investigate algorithmic questions that arise in the statistical problem of computing lines or hyperplanes of maximum regression depth among a set of n...time and o(n) space algorithm for computing a point of maximum depth in two dimensions, which has been improved to an o(nlogn) time algorithm by langerman and steiger (17...
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...we investigate algorithmic questions that arise in the statistical problem of computing lines or hyperplanes of maximum regression depth among a set of n...time and o(n) space algorithm for computing a point of maximum depth in two dimensions, which has been improved to an o(nlog n) time algorithm by langerman and steiger (discrete...
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...we investigate algorithmic questions that arise in the statistical problem of computing lines or
hyperplanes of maximum regression depth among a set of n...time and o(n) space
algorithm for computing a point of maximum depth in two dimensions, which has...
Published in 2002.
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...the sensors, present major challenges for such a vision to become...is maximized. this is the maximum lifetime data gathering problem. in this paper, we describe novel algorithms, with worst-case running times...
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...programs and are used extensively for code optimization and global data...o(n2m log(n2/m)) algorithm for finding a maximum cycle packing in any weighted reducible flow graph with n vertices and m arcs; our algorithm heavily relies on ramachandran's...
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...to vertical. we generalize hyperplane regression depth to k-flats for any,k between 0 and...exists a k-flat with depth at least a constant fraction of n. as a consequence, we derive a linear-time (1 +†)-approximation algorithm for the deepest flat....
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...paper, the authors propose an efficient algorithm for generating such decision forests. the algorithm uses an extended decision tree...authors have empirically evaluated the algorithm using 32 data sets for classification problems from the university...
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...were used in traditional exact algorithms. these algorithms are not efficient for large problems because significant amounts...to overcome these drawbacks, an efficient algorithm is proposed in this paper. the algorithm transforms a spare allocation problem...
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...the regression depth method (rdm) proposed by rousseeuw and hubert [rh99] plays an important role in the area of robust regression for a continuous response variable. christmann...machine (svm), [epsilon]-support vector regression and kernel logistic regression. in this paper connections between these methods from different disciplines are investigated for the case of pattern recognition...
Published in 2004.
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...process has been proved an efficient inference procedure for such analysis. however, computing the non- and semi-parametric maximum likelihood estimates (mles) can be...the available methods are not efficient. in this manuscript, we develop an efficient numerical algorithm stemming from the newton–raphson...