Results 51 to 60 of about 111,097 (310)
Analysis in Theory and Applications, 2009 Summary: This paper is an extension of earlier papers [\textit{R. Schaback}, in: New developments in approximation theory. 2nd international Dortmund meeting (IDoMAT) '98, Germany, February 23-27, 1998. Basel: Birkhäuser. ISNM, Int. Ser. Numer. Math. 132, 255--282 (1999; Zbl 0944.46017); J. Comput. Appl. Math.
Mouattamid, Mohammed, Schaback, Robert
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Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization
Sensors, 2012 When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s ...
Jing-Yu Yang +6 more
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Vertex Cover Kernelization Revisited: Upper and Lower Bounds for a Refined Parameter [PDF]
, 2012 An important result in the study of polynomial-time preprocessing shows that there is an algorithm which given an instance (G,k) of Vertex Cover outputs an equivalent instance (G',k') in polynomial time with the guarantee that G' has at most 2k' vertices
A. Schrijver +43 more
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, 2016 Kernel mean embeddings have recently attracted the attention of the machine learning community. They map measures $ $ from some set $M$ to functions in a reproducing kernel Hilbert space (RKHS) with kernel $k$. The RKHS distance of two mapped measures is a semi-metric $d_k$ over $M$. We study three questions.
Simon-Gabriel, C., Schölkopf, B.
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Multiple Kernel k-means with Incomplete Kernels [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernel matrices to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of base kernel matrices are absent.
Liu, Xinwang +9 more
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, 2014 Kernelization is a formalization of preprocessing for combinatorially hard problems. We modify the standard definition for kernelization, which allows any polynomial-time algorithm for the preprocessing, by requiring instead that the preprocessing runs ...
D. Lokshtanov +7 more
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A structural approach to kernels for ILPs: Treewidth and Total Unimodularity [PDF]
, 2015 Kernelization is a theoretical formalization of efficient preprocessing for NP-hard problems. Empirically, preprocessing is highly successful in practice, for example in state-of-the-art ILP-solvers like CPLEX.
A. Atamtürk +7 more
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A shortcut to (sun)flowers: Kernels in logarithmic space or linear time
, 2015 We investigate whether kernelization results can be obtained if we restrict kernelization algorithms to run in logarithmic space. This restriction for kernelization is motivated by the question of what results are attainable for preprocessing via simple ...
J Håstad +6 more
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Constraint Composite Graph-Based Weighted CSP Solvers: An Empirical Study
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceThe Weighted Constraint Satisfaction Problem (WCSP) is a very expressive framework for optimization problems. The Constraint Composite Graph (CCG) is a graphical representation of a given (Boolean) WCSP that facilitates its reduction to a Minimum ...
Orazio Rillo, T. K. Satish Kumar
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Beyond Bidimensionality: Parameterized Subexponential Algorithms on Directed Graphs [PDF]
, 2010 We develop two different methods to achieve subexponential time parameterized algorithms for problems on sparse directed graphs. We exemplify our approaches with two well studied problems.
Dorn, Frederic +4 more
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