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Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

Date 27-29 Oct. 1994

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  • Proceedings of 1994 Workshop on Information Theory and Statistics

    Publication Year: 1994
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    Freely Available from IEEE
  • Discussion of a statistical channel

    Publication Year: 1994
    Cited by:  Papers (5)
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    Freely Available from IEEE
  • Author index

    Publication Year: 1994
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    Freely Available from IEEE
  • Fisher information, stochastic complexity, and universal modeling

    Publication Year: 1994
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (100 KB)

    The main objective in universal modeling is to construct a process for a class of model processes which for long strings, generated by any of the models in the class, behaves like the data generating one. Hence, such a universal process may be taken as a representation of the entire model class to be used for statistical inference. If f(xn) denotes the probability or density it assigns ... View full abstract»

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  • Large deviations and the rate distortion theorem for Gibbs distributions

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (92 KB)

    Large deviation theory is used to obtain the rate distortion theorem for Gibbs distributions together with exponentially small error probabilities. Large deviation theorems provide asymptotically exponential upper and lower bounds on the probability that the empirical distribution under a Gibbs distribution deviates in variational norm from the marginal. In particular these hold if the Gibbs distr... View full abstract»

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  • Signal expansions for compression

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (100 KB)

    Signal expansions play a key role in practical compression schemes, from audio/image/video coding standards to current adaptives bases. Recent developments, especially related to wavelets series expansions, are reviewed, and current work on “best bases” is discussed View full abstract»

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  • Large deviations and consistent estimates for Gibbs random fields

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (76 KB)

    Large deviations estimates yield a convenient tool to study asymptotics of Gibbs fields. Applications to parametric estimation and detection of phase transition are given. Gibbs random fields provide pertinent statistical models for spacial data, where important features of the dependence structure can be captured in a very natural way View full abstract»

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  • The application of Akaike information criterion based pruning to nonparametric density estimates

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (84 KB)

    This paper examines the application of Akaike (1974) information criterion (AIC) based pruning to the refinement of nonparametric density estimates obtained via the adaptive mixtures (AM) procedure of Priebe (see JASA, vol.89, no.427, p.796-806, 1994) and Marchette. The paper details a new technique that uses these two methods in conjunction with one another to predict the appropriate number of te... View full abstract»

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  • Sample path description of Gauss Markov random fields

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (88 KB)

    We provide a characterization of Gauss Markov random fields in terms of partial differential equations with random forcing term. Our method consists of obtaining a concrete representation of an abstract stochastic partial differential equation using some results from the theory of vector measures View full abstract»

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  • Selection of best bases for classification and regression

    Publication Year: 1994
    Cited by:  Papers (33)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (116 KB)

    We describe extensions to the “best-basis” method to select orthonormal bases suitable for signal classification (or regression) problems from a collection of orthonormal bases using the relative entropy (or regression errors). Once these bases are selected, the most significant coordinates are fed into a traditional classifier (or regression method) such as linear discriminant analysi... View full abstract»

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  • The finite-sample risk of the k-nearest-neighbor classifier under the Lp metric

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (88 KB)

    The finite-sample risk of the k-nearest neighbor classifier that uses an L2 distance function is examined. For a family of classification problems with smooth distributions in Rn, the risk can be represented as an asymptotic expansion in inverse powers of the n-th root of the reference-sample size. The leading coefficients of this expansion suggest that the Euclidean or L... View full abstract»

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  • Mismatched encoding in rate distortion theory

    Publication Year: 1994
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (48 KB)

    Summary form only given. A length n block code C of size 2nR over a finite alphabet χˆ0 is used to encode a memoryless source over a finite alphabet χ. A length n source sequence x is described by the index i of the codeword xˆ0 (i) that is nearest to x according to the single-letter distortion function d0(x,xˆ0). Based... View full abstract»

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  • Large deviations in information theory and statistics

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (76 KB)

    Large deviations theory, a branch of probability theory that deals with estimates of probabilities of very rare events has close links with topics in information theory and in statistics. Some of these connections are explored View full abstract»

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  • Finite-precision intrinsic randomness and source resolvability

    Publication Year: 1994
    Cited by:  Papers (1)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (100 KB)

    Random number generators are important devices in randomized algorithms, Monte-Carlo methods, and in simulation studies of random systems. A random number generator is usually modeled as a random source emitting independent, equally likely random bits. In practice, the random source one has at hand can deviate from this idealized model, and the random number generator operates by applying a determ... View full abstract»

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  • Image coding via bintree segmentation and texture VQ

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (120 KB)

    Image compression is often approached from an angle of statistical image classification. For instance, VQ-based image coding methods compress image data by classifying image blocks into representative two-dimensional patterns (codewords) that statistically approximate the original data. Another image compression approach that naturally relates to image classification is segmentation-based image co... View full abstract»

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  • Non-parametric discriminatory power

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (104 KB)

    Discriminatory power is the relative usefulness of a feature for classification. Traditionally feature-selection techniques have defined discriminatory power in terms of a particular classifier. Non-parametric discriminately power allows feature selection to be based on the structure of the data rather than on the requirements of any one classifier. In previous research, we have defined a metric f... View full abstract»

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  • Estimation and prediction for (mostly Gaussian) Markov fields in the continuum

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (80 KB)

    We present a survey of design problems and results that arise in the prediction and parameter estimation of stochastic partial differential equations (SPDES). The aim is to better understand some unavoidable errors that occur in the discretization of SPDEs, and available methods for minimizing these errors View full abstract»

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  • Markov random fields on graphs for natural languages

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (108 KB)

    The use of model-based methods for data compression for English dates back at least to Shannon's Markov chain (n-gram) models, where the probability of the next word given all previous words equals the probability of the next word given the previous n-1 words. A second approach seeks to model the hierarchical nature of language via tree graph structures arising from a context-free language (CFL). ... View full abstract»

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  • Improved Ziv-Zakai lower bound for vector parameter estimation

    Publication Year: 1994
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (92 KB)

    The Ziv-Zakai (1969) bounds on the mean square error (MSE) in parameter estimation are some of the tightest available bounds. These bounds relate the MSE in the estimation problem to the probability of error in a binary hypothesis testing problem. The original Bayesian version derived by Ziv and Zakai, and improvements by Chazan, Zakai and Ziv (1975) and Bellini and Tartara (1974) are applicable t... View full abstract»

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  • The entropy strategy for shape recognition

    Publication Year: 1994
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (108 KB)

    We consider a computational strategy for shape recognition based on choosing “tests” one at a time in order to remove as much uncertainty as possible about the true hypothesis. The approach is compared with other recognition paradigms in computer vision and illustrated by attempting to classify handwritten digits and track roads from satellite images View full abstract»

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  • Tracking long-range dependencies with wavelets

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (112 KB)

    Long-range dependent processes exhibit features, such as 1/f spectra, for which wavelets offer versatile tools and provide a unifying framework. This efficiency is demonstrated on both continuous processes, point processes and filtered point processes. The fractal shot noise model is also considered View full abstract»

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  • MMSE parameter estimation of exponentially damped sinusoids

    Publication Year: 1994
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (88 KB)

    An efficient iterative MMSE algorithm that estimates the parameters of exponentially damped sinusoids embedded in white Gaussian noise is proposed View full abstract»

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  • Markov chains and random walks in data communication receivers

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (96 KB)

    In many data communication receivers up/down counters are used as a critical part of the processing to determine whether the symbol timing and/or carrier phase tracking phase-locked loops are in-lock or out-of-lock, and it is necessary to calculate the various probabilities for true and false indications of in-lock or out-of-lock. A random walk along a line (which is viewed as a Markov chain) is a... View full abstract»

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  • Nonparametric classifier design using vector quantization

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (104 KB)

    VQ-based method is developed as an effective data reduction technique for nonparametric classifier design. This new technique, while insisting on competitive classification accuracy, is found to overcome the usual disadvantage of traditional nonparametric classifiers of being computationally complex and of requiring large amounts of computer storage View full abstract»

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  • Constructing wavelets from desired signal functions

    Publication Year: 1994
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (88 KB)

    A limitation to wavelet design is the inability to construct orthonormal wavelets that match or are “tuned” to a desired signal. This paper develops a technique for constructing an orthonormal wavelet that is optimized in the least squares sense, and whose associated scaling function generates an orthonormal multiresolution analysis (OMRA) View full abstract»

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