Results 21 to 30 of about 394,763 (294)
Advances in Data Science and Adaptive Analysis, 2019 A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets)
Hedi Xia, Héctor D. Ceniceros
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Engineering Kernelization for Maximum Cut [PDF]
Workshop on Algorithm Engineering and Experimentation, 2019 Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules.
D. Ferizović +5 more
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Exact and kernelization algorithms for Closet String
Selecciones Matemáticas, 2020 In this paper we address CLOSEST STRING problem that arises in web searching, coding theory and computational molecular biology. To solve it is to find a string that minimizes the maximum Hamming distance from a given set of strings. CLOSEST STRING is an
Omar Latorre Vilca
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An Overview of Kernelization Algorithms for Graph Modification Problems
Tsinghua Science and Technology, 2014 Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion ...
Yunlong Liu, Jianxin Wang, Jiong Guo
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Matching Cut: Kernelization, Single-Exponential Time FPT, and Exact Exponential Algorithms
International Symposium on Parameterized and Exact Computation, 2020 In a graph, a matching cut is an edge cut that is a matching. Matching Cut , which is known to be NP-complete, is the problem of deciding whether or not a given graph G has a matching cut.
Christian Komusiewicz +2 more
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Scalable Kernelization for Maximum Independent Sets [PDF]
Workshop on Algorithm Engineering and Experimentation, 2017 The most efficient algorithms for finding maximum independent sets in both theory and practice use reduction rules to obtain a much smaller problem instance called a kernel.
Demian Hespe +2 more
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Unsupervised Transfer Learning via Relative Distance Comparisons
IEEE Access, 2020 Primitive machine learning method such as Support Vector Machine (SVM) or k-Nearest Neighbor (k-NN) faces a major challenge when its training and test data is distributed with large-scale variations in lighting conditions, color, backgrounds, size, etc ...
Rakesh Kumar Sanodiya, Leehter Yao
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What Is Known About Vertex Cover Kernelization? [PDF]
Adventures Between Lower Bounds and Higher Altitudes, 2018 We are pleased to dedicate this survey on kernelization of the Vertex Cover problem, to Professor Juraj Hromkovic on the occasion of his 60th birthday. The Vertex Cover problem is often referred to as the Drosophila of parameterized complexity. It enjoys
M. Fellows +4 more
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Hierarchical Kernel and Sub-kernels [PDF]
American Review of Mathematics and Statistics, 2017 This paper shows the theoretical development of hierarchy by kernels and an algorithm used to obtain an interesting class or partition from a hierarchy. Also shown is the theorem about the Kernels Optimal Criterion and how it is expressed as a function of the masses of the points of the vector space and product scale points, the inertia of the cloud ...
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A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation
Remote Sensing, 2015 In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered.
Ying-Nong Chen +4 more
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