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TIP
2010
141views more  TIP 2010»
14 years 4 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina
ICPR
2008
IEEE
15 years 10 months ago
Fast protein homology and fold detection with sparse spatial sample kernels
In this work we present a new string similarity feature, the sparse spatial sample (SSS). An SSS is a set of short substrings at specific spatial displacements contained in the or...
Pai-Hsi Huang, Pavel P. Kuksa, Vladimir Pavlovic
ICMLA
2008
14 years 11 months ago
Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins
In this paper, we introduce two new formulations for multi-class multi-kernel relevance vector machines (mRVMs) that explicitly lead to sparse solutions, both in samples and in nu...
Theodoros Damoulas, Yiming Ying, Mark A. Girolami,...
ICIP
2006
IEEE
15 years 11 months ago
Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data
We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected s...
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
TNN
2010
176views Management» more  TNN 2010»
14 years 4 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao