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Exploiting $\mathbf{c}$-Closure in Kernelization Algorithms for Graph Problems
Embedded Systems and Applications, 2020A graph is c-closed if every pair of vertices with at least c common neighbors is adjacent. The c-closure of a graph G is the smallest number such that G is c-closed. Fox et al.
T. Koana +2 more
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Approximate Turing Kernelization for Problems Parameterized by Treewidth
Embedded Systems and Applications, 2020We extend the notion of lossy kernelization, introduced by Lokshtanov et al. [STOC 2017], to approximate Turing kernelization. An $\alpha$-approximate Turing kernel for a parameterized optimization problem is a polynomial-time algorithm that, when given ...
Eva-Maria C. Hols +2 more
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2011 International Conference on Computer Vision, 2011
Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization.
Tinne Tuytelaars +3 more
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Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization.
Tinne Tuytelaars +3 more
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DiJiang: Efficient Large Language Models through Compact Kernelization
International Conference on Machine LearningIn an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mechanisms typically necessitate extensive retraining, which is impractical for
Hanting Chen +4 more
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International Workshop on Graph-Theoretic Concepts in Computer Science
The notion of a (polynomial) kernelization from parameterized complexity is a well-studied model for efficient preprocessing for hard computational problems.
Leonid Antipov, Stefan Kratsch
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The notion of a (polynomial) kernelization from parameterized complexity is a well-studied model for efficient preprocessing for hard computational problems.
Leonid Antipov, Stefan Kratsch
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On polynomial kernelization for Stable Cutset
International Workshop on Graph-Theoretic Concepts in Computer ScienceA stable cutset in a graph $G$ is a set $S\subseteq V(G)$ such that vertices of $S$ are pairwise non-adjacent and such that $G-S$ is disconnected, i.e., it is both stable (or independent) set and a cutset (or separator). Unlike general cutsets, it is $NP$
Stefan Kratsch, Van Bang Le
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Partitionable Kernels for Mapping Kernels
2011 IEEE 11th International Conference on Data Mining, 2011Many of tree kernels in the literature are designed tanking advantage of the mapping kernel framework. The most important advantage of using this framework is that we have a strong theorem to examine positive definiteness of the resulting tree kernels.
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Kernelization and approximation of distance-r independent sets on nowhere dense graphs
European journal of combinatorics (Print), 2018For a positive integer $r$, a distance-$r$ independent set in an undirected graph $G$ is a set $I\subseteq V(G)$ of vertices pairwise at distance greater than $r$, while a distance-$r$ dominating set is a set $D\subseteq V(G)$ such that every vertex of ...
Michał Pilipczuk, S. Siebertz
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Multiple kernel clustering with corrupted kernels
Neurocomputing, 2017Abstract Multiple kernel clustering (MKC) algorithms usually learn an optimal kernel from a group of pre-specified base kernels to improve the clustering performance. However, we observe that existing MKC algorithms do not well handle the situation that kernels are corrupted with noise and outliers.
Teng Li 0010 +4 more
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Neurocomputing, 2019
This paper proposes kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters which learn the variable weights and adjust the cluster weights automatically.
Junyan Liu +4 more
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This paper proposes kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters which learn the variable weights and adjust the cluster weights automatically.
Junyan Liu +4 more
semanticscholar +1 more source