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Kernelization of Constraint Satisfaction Problems: A Study Through Universal Algebra
International Conference on Principles and Practice of Constraint Programming, 2017A kernelization algorithm for a computational problem is a procedure which compresses an instance into an equivalent instance whose size is bounded with respect to a complexity parameter.
Victor Lagerkvist, Magnus Wahlström
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Communications of the ACM, 2018
Choosing between programming in the kernel or in user space.
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Choosing between programming in the kernel or in user space.
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Triangle packing in (sparse) tournaments: approximation and kernelization
Embedded Systems and Applications, 2017Given a tournament T and a positive integer k, the C_3-Pakcing-T problem asks if there exists a least k (vertex-)disjoint directed 3-cycles in T. This is the dual problem in tournaments of the classical minimal feedback vertex set problem.
S. Bessy, M. Bougeret, Jocelyn Thiebaut
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Semisupervised Kernel Matrix Learning by Kernel Propagation
IEEE Transactions on Neural Networks, 2010The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency ...
Enliang Hu +3 more
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Reliable Computing, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Hierarchical kernels in deep kernel learning
J. Mach. Learn. Res., 2023Summary: Kernel methods are built upon the mathematical theory of reproducing kernels and reproducing kernel Hilbert spaces. They enjoy good interpretability thanks to the solid mathematical foundation. Recently, motivated by deep neural networks in deep learning, which construct learning functions by successive compositions of activation functions and
Wentao Huang, Houbao Lu, Haizhang Zhang
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Neighborhood complexity and kernelization for nowhere dense classes of graphs
International Colloquium on Automata, Languages and Programming, 2016We prove that whenever G is a graph from a nowhere dense graph class C, and A is a subset of vertices of G, then the number of subsets of A that are realized as intersections of A with r-neighborhoods of vertices of G is at most f(r,eps)|A|^(1+eps ...
Kord Eickmeyer +6 more
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Linear-time Kernelization for Feedback Vertex Set
International Colloquium on Automata, Languages and Programming, 2016In this paper, we propose an algorithm that, given an undirected graph $G$ of $m$ edges and an integer $k$, computes a graph $G'$ and an integer $k'$ in $O(k^4 m)$ time such that (1) the size of the graph $G'$ is $O(k^2)$, (2) $k'\leq k$, and (3) $G$ has
Yoichi Iwata
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18th International Conference on Pattern Recognition (ICPR'06), 2006
In this work we introduce a new methodology to build a kernel matrix from a collection of kernels. The key idea is to build an unique kernel that eliminates spurious differences between kernels. We propose a method based on the Procrustes problems that uses the Alternating Projections method to minimize a certain error measure.
Isaac Martín de Diego, Alberto Muñoz
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In this work we introduce a new methodology to build a kernel matrix from a collection of kernels. The key idea is to build an unique kernel that eliminates spurious differences between kernels. We propose a method based on the Procrustes problems that uses the Alternating Projections method to minimize a certain error measure.
Isaac Martín de Diego, Alberto Muñoz
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Smaller parameters for vertex cover kernelization
International Symposium on Parameterized and Exact Computation, 2017We revisit the topic of polynomial kernels for Vertex Cover relative to structural parameters. Our starting point is a recent paper due to Fomin and Str{\o}mme [WG 2016] who gave a kernel with $\mathcal{O}(|X|^{12})$ vertices when $X$ is a vertex set ...
Eva-Maria C. Hols, Stefan Kratsch
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