Ξ-filters

By J. Preston. 1983

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In Signal Processing

Citations in the corpus

On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models (2019, methodology): “The persistent chains can be initialized from noise as in [36, 33] or from data samples as in Persistent Contrastive Divergence (PCD) [25].”
Meshfree Methods: Progress Made after 20 Years (2017, unknown): “Adaptive refinement can also be implemented with relative ease compared to the conventional mesh-based methods (Liu and Chen 1995; Liu et al. 1997a; Rabczuk and Belytschko 2005; Rabczuk and Samaniego 2008; You et al. 2003).”
Meshless methods: A review and computer implementation aspects (2008, methodology): “Other estimators and adaptive methods are proposed in [59,66,87,86,102,52,51,53].”
Fields of Experts: a framework for learning image priors (2005, methodology): “Zhu and Mumford took a step toward more practical MRFs with the introduction of the FRAME model [27], which allowed MRF priors to be learned from training data.”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, methodology): “They are more efficient than wrapper methods while providing comparable accuracy [8,16,20,27,30,39,45,48].”
Image Decomposition and Restoration Using Total Variation Minimization and the H1 (2003, methodology): “Also, texture modeling by statistical methods was proposed by Zhu, Wu, and Mumford in [23], [24] and by Casadei, Mitter, and Perona in [5], among many others.”
Adaptive element-free Galerkin method applied to the limit analysis of plates (2010, background): “In order to reduce computational cost and to ensure consistency of the MLS approximation, matrix A in the shape functions of existing nodes are reconstructed locally as [22]:”
Prior Learning and Gibbs Reaction-Diffusion (1997, methodology): “In previous work on modeling textures, we proposed a new class of Gibbs distributions of the following form [40, 41], p(I; ; S) = 1 Z e U(I; ;S); (2) U(I; ; S) = K X =1 X (x;y) ( )((F ( ) I)(x; y)): (3) In the above equation, S = fF (1); F (2); :::; F (K)g is a set of linear lters, and = f (1)(); (2”
Building a Telescope to Look Into High-Dimensional Image Spaces (2018, methodology): “Sparse basis functions can effectively capture the features of texton images, while MRF distributions are more suitable for representing texture patterns [39, 47, 53].”
Consistency-based search in feature selection (2003, background): “Recent papers such as [2,9,16,17,22,43] address some of the existing issues of feature selection.”
Detecting hidden messages using higher-order statistical models (2002, methodology): “In contrast, the approach taken here relies on building higher-order statistical models for natur l images [9, 15, 22, 10, 17] and looking for deviations from these models.”
Data Preprocessing for Supervised Leaning (2007, unknown): “BDSFS (Boosted Decision Stump FS) uses boosted decision stumps as the pre-processor to discover the feature subset [9].”