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A Survey of Program Slicing Techniques
 JOURNAL OF PROGRAMMING LANGUAGES
, 1995
"... A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser in 197 ..."
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Cited by 790 (10 self)
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A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
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Cited by 2395 (37 self)
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the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M
BIRCH: an efficient data clustering method for very large databases
 In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD
, 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multidir nensional clataset. Prior work does not adequately address the problem of ..."
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Cited by 576 (2 self)
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is also the first clustering algorithm proposerl in the database area to handle “noise) ’ (data points that are not part of the underlying pattern) effectively. We evaluate BIRCH’S time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a
A NEW POLYNOMIALTIME ALGORITHM FOR LINEAR PROGRAMMING
 COMBINATORICA
, 1984
"... We present a new polynomialtime algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than the ell ..."
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Cited by 860 (3 self)
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We present a new polynomialtime algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than
MAFFT version 5: improvement in accuracy of multiple sequence alignment
 NUCLEIC ACIDS RES
, 2005
"... The accuracy of multiple sequence alignment program MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, HINSi, FINSi and GINSi, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed high ..."
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Cited by 801 (5 self)
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the effect of the number of homologues included in an alignment. For a multiple alignment consisting of 8 sequences with low similarity, the accuracy was improved (2–10 percentage points) when the sequences were aligned together with dozens of their close homologues (Evalue, 105–1020) collected from a
Object Recognition from Local ScaleInvariant Features
"... An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in ..."
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Cited by 2739 (13 self)
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in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients
The Nature and Growth of Vertical Specialization in World Trade
 Journal of International Economics
"... Abstract: Dramatic changes are occurring in the nature of international trade. Production processes increasingly involve a sequential, vertical trading chain stretching across many countries, with each country specializing in particular stages of a good’s production sequence. We document a key aspe ..."
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Cited by 481 (20 self)
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aspect of these vertical linkages – the use of imported inputs in producing goods that are exported – which we call vertical specialization. Using inputoutput tables from the OECD and emerging market countries we estimate that vertical specialization accounts for up to 30 % of world exports, and has
Estimating the Error at Given Test Input Points for Linear Regression
"... In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so t ..."
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In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so
Estimating the Error at Given Test Input Points for Linear Regression
"... In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so t ..."
Abstract
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In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so
Applications of Random Sampling in Computational Geometry, II
 Discrete Comput. Geom
, 1995
"... We use random sampling for several new geometric algorithms. The algorithms are "Las Vegas," and their expected bounds are with respect to the random behavior of the algorithms. These algorithms follow from new general results giving sharp bounds for the use of random subsets in geometric ..."
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Cited by 432 (12 self)
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gives fast expected times for random input points. Another algorithm computes the diameter of a set of n...
Results 11  20
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