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Signal Processing, IEEE Transactions on

Issue 8 • Date Aug. 2004

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Displaying Results 1 - 25 of 26
  • Table of contents

    Publication Year: 2004, Page(s): c1
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  • IEEE Transactions on Signal Processing publication information

    Publication Year: 2004, Page(s): c2
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  • Message from the Editor-in-Chief: Best Paper Award Recipients

    Publication Year: 2004, Page(s):2149 - 2151
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  • Guest Editorial: Special Issue on Machine Learning Methods in Signal Processing

    Publication Year: 2004, Page(s): 2152
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  • Sparse Bayesian learning for basis selection

    Publication Year: 2004, Page(s):2153 - 2164
    Cited by:  Papers (205)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB) | HTML iconHTML

    Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we adapt SBL to the signal processing problem of... View full abstract»

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  • Online learning with kernels

    Publication Year: 2004, Page(s):2165 - 2176
    Cited by:  Papers (203)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB) | HTML iconHTML

    Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time applications. In this paper, we consider ... View full abstract»

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  • Linear minimax regret estimation of deterministic parameters with bounded data uncertainties

    Publication Year: 2004, Page(s):2177 - 2188
    Cited by:  Papers (45)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (592 KB) | HTML iconHTML

    We develop a new linear estimator for estimating an unknown parameter vector x in a linear model in the presence of bounded data uncertainties. The estimator is designed to minimize the worst-case regret over all bounded data vectors, namely, the worst-case difference between the mean-squared error (MSE) attainable using a linear estimator that does not know the true parameters x and the optimal M... View full abstract»

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  • Learning graphical models for stationary time series

    Publication Year: 2004, Page(s):2189 - 2199
    Cited by:  Papers (26)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB) | HTML iconHTML

    Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed naturally in the frequency domain, leading to interesting families of structured time series models that are complementary to families defined in the time domain. In this paper, we pre... View full abstract»

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  • Stochastic correlative learning algorithms

    Publication Year: 2004, Page(s):2200 - 2209
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (392 KB) | HTML iconHTML

    This paper addresses stochastic correlative learning as the basis for a broadly defined class of statistical learning algorithms known collectively as the algorithm of pattern extraction (ALOPEX) family. Starting with the neurobiologically motivated Hebb's rule, the two conventional forms of the ALOPEX algorithm are derived, followed by a modified variant designed to improve the convergence speed.... View full abstract»

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  • Geodesic entropic graphs for dimension and entropy estimation in manifold learning

    Publication Year: 2004, Page(s):2210 - 2221
    Cited by:  Papers (69)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (600 KB) | HTML iconHTML

    In the manifold learning problem, one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper, we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample points. Specifically, we view the samp... View full abstract»

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  • A variational approach for Bayesian blind image deconvolution

    Publication Year: 2004, Page(s):2222 - 2233
    Cited by:  Papers (42)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB) | HTML iconHTML

    In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology rea... View full abstract»

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  • Nonparametric hypothesis tests for statistical dependency

    Publication Year: 2004, Page(s):2234 - 2249
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (760 KB) | HTML iconHTML

    Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multitarget tracking and computer vision. In this paper, we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations of variables into mutually independent subset... View full abstract»

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  • Kernel-based feature extraction with a speech technology application

    Publication Year: 2004, Page(s):2250 - 2263
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (664 KB) | HTML iconHTML

    Kernel-based nonlinear feature extraction and classification algorithms are a popular new research direction in machine learning. This paper examines their applicability to the classification of phonemes in a phonological awareness drilling software package. We first give a concise overview of the nonlinear feature extraction methods such as kernel principal component analysis (KPCA), kernel indep... View full abstract»

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  • TEMPLAR: a wavelet-based framework for pattern learning and analysis

    Publication Year: 2004, Page(s):2264 - 2274
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (616 KB) | HTML iconHTML

    Recovering a pattern or image from a collection of noisy and misaligned observations is a challenging problem that arises in image processing and pattern recognition. This paper presents an automatic, wavelet-based approach to this problem. Despite the success of wavelet decompositions in other areas of statistical signal and image processing, most wavelet-based image models are inadequate for mod... View full abstract»

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  • The kernel recursive least-squares algorithm

    Publication Year: 2004, Page(s):2275 - 2285
    Cited by:  Papers (222)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (432 KB) | HTML iconHTML

    We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a high-dimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum mean-squared-error solutions to nonlinear least-squares problems that are frequently encountered in signal processing applications. In order to regularize sol... View full abstract»

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  • Iterative learning algorithms for linear Gaussian observation models

    Publication Year: 2004, Page(s):2286 - 2297
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (584 KB) | HTML iconHTML

    In this paper, we consider a signal/parameter estimation problem that is based on a linear model structure and a given setting of statistical models with unknown hyperparameters. We consider several combinations of Gaussian and Laplacian models. We develop iterative algorithms based on two typical machine learning methods - the evidence-based method and the integration-based method - to deal with ... View full abstract»

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  • SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems

    Publication Year: 2004, Page(s):2298 - 2307
    Cited by:  Papers (39)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (296 KB) | HTML iconHTML

    This paper addresses the problem of multiple-input multiple-output (MIMO) frequency nonselective channel estimation. We develop a new method for multiple variable regression estimation based on Support Vector Machines (SVMs): a state-of-the-art technique within the machine learning community for regression estimation. We show how this new method, which we call M-SVR, can be efficiently applied. Th... View full abstract»

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  • Likelihood based hierarchical clustering

    Publication Year: 2004, Page(s):2308 - 2321
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (464 KB) | HTML iconHTML

    This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves. In certain problems, this generative model naturally captures the physical mechanisms responsible for rela... View full abstract»

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  • Minimum probability of error image retrieval

    Publication Year: 2004, Page(s):2322 - 2336
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (568 KB) | HTML iconHTML

    We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower- and upper-bounded by functions of the Bayes and density estimation errors, and the impact of the components of the retrieva... View full abstract»

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  • Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding

    Publication Year: 2004, Page(s):2337 - 2347
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (368 KB) | HTML iconHTML

    Zero-delay lossy source coding schemes are considered for both individual sequences and random sources. Performance is measured by the distortion redundancy, which is defined as the difference between the normalized cumulative mean squared distortion of the scheme and the normalized cumulative distortion of the best scalar quantizer of the same rate that is matched to the entire sequence to be enc... View full abstract»

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  • Applications of support vector machines to speech recognition

    Publication Year: 2004, Page(s):2348 - 2355
    Cited by:  Papers (82)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB) | HTML iconHTML

    Recent work in machine learning has focused on models, such as the support vector machine (SVM), that automatically control generalization and parameterization as part of the overall optimization process. In this paper, we show that SVMs provide a significant improvement in performance on a static pattern classification task based on the Deterding vowel data. We also describe an application of SVM... View full abstract»

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  • IEEE Transactions on Signal Processing Edics

    Publication Year: 2004, Page(s): 2356
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  • IEEE Transactions on Signal Processing Information for authors

    Publication Year: 2004, Page(s):2357 - 2358
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  • IEEE copyright form

    Publication Year: 2004, Page(s):2359 - 2360
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  • IEEE Signal Processing Society Information

    Publication Year: 2004, Page(s):c3 - o3
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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens