Results 31 to 40 of about 806,396 (338)
Convergence rates of Kernel Conjugate Gradient for random design regression [PDF]
, 2016 We prove statistical rates of convergence for kernel-based least squares regression from i.i.d. data using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping.
Blanchard, Gilles, Krämer, Nicole
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Weighted Nash Inequalities [PDF]
, 2010 Nash or Sobolev inequalities are known to be equivalent to ultracontractive properties of Markov semigroups, hence to uniform bounds on their kernel densities.
Bakry, Dominique +3 more
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Attributed Relational SIFT-Based Regions Graph: Concepts and Applications
Machine Learning and Knowledge Extraction, 2020 In the real world, structured data are increasingly represented by graphs. In general, the applications concern the most varied fields, and the data need to be represented in terms of local and spatial connections.
Mario Manzo
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Enumeration of convex polyominoes using the ECO method [PDF]
Discrete Mathematics & Theoretical Computer Science, 2003 ECO is a method for the enumeration of classes of combinatorial objects based on recursive constructions of such classes. In the first part of this paper we present a construction for the class of convex polyominoes based on the ECO method.
A. Del Lungo +3 more
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Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation
Sensors, 2018 The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation
Xiaobo Chen +4 more
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Analytic Combinatorics of Lattice Paths: Enumeration and Asymptotics for the Area [PDF]
Discrete Mathematics & Theoretical Computer Science, 2006 This paper tackles the enumeration and asymptotics of the area below directed lattice paths (walks on $\mathbb{N}$ with a finite set of jumps). It is a nice surprise (obtained via the "kernel method'') that the generating functions of the moments of the ...
Cyril Banderier, Bernhard Gittenberger
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Kernel methods in machine learning
The Annals of Statistics, 2008 We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of ...
Hofmann, Thomas +2 more
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Information Theory With Kernel Methods
IEEE Transactions on Information Theory, 2023 We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of Shannon entropy and relative entropy, and share many of their properties.
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Entropy, 2019 Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency.
Xuejun Zhao +4 more
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Spectral Norm of Random Kernel Matrices with Applications to Privacy [PDF]
, 2015 Kernel methods are an extremely popular set of techniques used for many important machine learning and data analysis applications. In addition to having good practical performances, these methods are supported by a well-developed theory.
Kasiviswanathan, Shiva Prasad +1 more
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