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Abstract: Historical documents such as old books and manuscripts have a high aesthetic value and highly appreciated. Unfortunately, there are some documents cannot be read due to quality problems like faded paper, ink expand, uneven colour tone, torn paper and other elements disruption such as the existence of small sp... |
Title: Codeco: A Grammar Notation for Controlled Natural Language in Predictive Editors |
Abstract: Existing grammar frameworks do not work out particularly well for controlled natural languages (CNL), especially if they are to be used in predictive editors. I introduce in this paper a new grammar notation, called Codeco, which is designed specifically for CNLs and predictive editors. Two different parsers ... |
Title: Weak consistency of Markov chain Monte Carlo methods |
Abstract: Markov chain Monte Calro methods (MCMC) are commonly used in Bayesian statistics. In the last twenty years, many results have been established for the calculation of the exact convergence rate of MCMC methods. We introduce another rate of convergence for MCMC methods by approximation techniques. This rate can... |
Title: Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments |
Abstract: To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments. |
Title: A Parametric Level Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual Energy Computed Tomography |
Abstract: Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy proces... |
Title: Improved Edge Awareness in Discontinuity Preserving Smoothing |
Abstract: Discontinuity preserving smoothing is a fundamentally important procedure that is useful in a wide variety of image processing contexts. It is directly useful for noise reduction, and frequently used as an intermediate step in higher level algorithms. For example, it can be particularly useful in edge detecti... |
Title: Linear programming problems for l_1- optimal frontier estimation |
Abstract: We propose new optimal estimators for the Lipschitz frontier of a set of points. They are defined as kernel estimators being sufficiently regular, covering all the points and whose associated support is of smallest surface. The estimators are written as linear combinations of kernel functions applied to the p... |
Title: Linear programming problems for frontier estimation |
Abstract: We propose new estimates for the frontier of a set of points. They are defined as kernel estimates covering all the points and whose associated support is of smallest surface. The estimates are written as linear combinatio- ns of kernel functions applied to the points of the sample. The coefficients of the li... |
Title: Extreme values and kernel estimates of point processes boundaries |
Abstract: We present a method for estimating the edge of a two-dimensional bounded set, given a finite random set of points drawn from the interior. The estimator is based both on a Parzen-Rosenblatt kernel and extreme values of point processes. We give conditions for various kinds of convergence and asymptotic normali... |
Title: Projection estimates of point processes boundaries |
Abstract: We present a method for estimating the edge of a two-dimensional bounded set, given a finite random set of points drawn from the interior. The estimator is based both on projections on C^1 bases and on extreme points of the point process. We give conditions on the Dirichlet's kernel associated to the C^1 base... |
Title: Extreme value and Haar series estimates of point process boundaries |
Abstract: We present a new method for estimating the edge of a two-dimensional bounded set, given a finite random set of points drawn from the interior. The estimator is based both on Haar series and extreme values of the point process. We give conditions for various kind of convergence and we obtain remarkably differe... |
Title: Frontier estimation via kernel regression on high power-transformed data |
Abstract: We present a new method for estimating the frontier of a multidimensional sample. The estimator is based on a kernel regression on the power-transformed data. We assume that the exponent of the transformation goes to infinity while the bandwidth of the kernel goes to zero. We give conditions on these two para... |
Title: On Empirical Entropy |
Abstract: We propose a compression-based version of the empirical entropy of a finite string over a finite alphabet. Whereas previously one considers the naked entropy of (possibly higher order) Markov processes, we consider the sum of the description of the random variable involved plus the entropy it induces. We assu... |
Title: Automatic Step Size Selection in Random Walk Metropolis Algorithms |
Abstract: Practitioners of Markov chain Monte Carlo (MCMC) may hesitate to use random walk Metropolis-Hastings algorithms, especially variable-at-a-time algorithms with many parameters, because these algorithms require users to select values of tuning parameters (step sizes). These algorithms perform poorly if the step... |
Title: Internal Constraints of the Trifocal Tensor |
Abstract: The fundamental matrix and trifocal tensor are convenient algebraic representations of the epipolar geometry of two and three view configurations, respectively. The estimation of these entities is central to most reconstruction algorithms, and a solid understanding of their properties and constraints is there... |
Title: Counting with Combined Splitting and Capture-Recapture Methods |
Abstract: We apply the splitting method to three well-known counting problems, namely 3-SAT, random graphs with prescribed degrees, and binary contingency tables. We present an enhanced version of the splitting method based on the capture-recapture technique, and show by experiments the superiority of this technique fo... |
Title: Smoothed extreme value estimators of non-uniform point processes boundaries with application to star-shaped supports estimation |
Abstract: We address the problem of estimating the edge of a bounded set in R^d given a random set of points drawn from the interior. Our method is based on a transformation of estimators dedicated to uniform point processes and obtained by smoothing some of its bias corrected extreme points. An application to the esti... |
Title: Auto-associative models, nonlinear Principal component analysis, manifolds and projection pursuit |
Abstract: In this paper, auto-associative models are proposed as candidates to the generalization of Principal Component Analysis. We show that these models are dedicated to the approximation of the dataset by a manifold. Here, the word "manifold" refers to the topology properties of the structure. The approximating ma... |
Title: Bias-reduced extreme quantiles estimators of Weibull-tail distributions |
Abstract: In this paper, we consider the problem of estimating an extreme quantile of a Weibull tail-distribution. The new extreme quantile estimator has a reduced bias compared to the more classical ones proposed in the literature. It is based on an exponential regression model that was introduced in Diebolt et al. (2... |
Title: Quasi-conjugate Bayes estimates for GPD parameters and application to heavy tails modelling |
Abstract: We present a quasi-conjugate Bayes approach for estimating Generalized Pareto Distribution (GPD) parameters, distribution tails and extreme quantiles within the Peaks-Over-Threshold framework. Damsleth conjugate Bayes structure on Gamma distributions is transfered to GPD. Posterior estimates are then computed... |
Title: Frontier estimation with local polynomials and high power-transformed data |
Abstract: We present a new method for estimating the frontier of a sample. The estimator is based on a local polynomial regression on the power-transformed data. We assume that the exponent of the transformation goes to infinity while the bandwidth goes to zero. We give conditions on these two parameters to obtain almo... |
Title: Decentralized Online Learning Algorithms for Opportunistic Spectrum Access |
Abstract: The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are $M$ users and $N$ arms (channels) that each offer... |
Title: U-Sem: Semantic Enrichment, User Modeling and Mining of Usage Data on the Social Web |
Abstract: With the growing popularity of Social Web applications, more and more user data is published on the Web everyday. Our research focuses on investigating ways of mining data from such platforms that can be used for modeling users and for semantically augmenting user profiles. This process can enhance adaptation... |
Title: Towards an automated query modification assistant |
Abstract: Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method ... |
Title: Estimation procedures for a semiparametric family of bivariate copulas |
Abstract: In this paper, we propose simple estimation methods dedicated to a semiparametric family of bivariate copulas. These copulas can be simply estimated through the estimation of their univariate generating function. We take profit of this result to estimate the associated measures of association as well as the h... |
Title: Functional nonparametric estimation of conditional extreme quantiles |
Abstract: We address the estimation of quantiles from heavy-tailed distributions when functional covariate information is available and in the case where the order of the quantile converges to one as the sample size increases. Such "extreme" quantiles can be located in the range of the data or near and even beyond the ... |
Title: Normal form backward induction for decision trees with coherent lower previsions |
Abstract: We examine normal form solutions of decision trees under typical choice functions induced by lower previsions. For large trees, finding such solutions is hard as very many strategies must be considered. In an earlier paper, we extended backward induction to arbitrary choice functions, yielding far more effici... |
Title: Gaussian Robust Classification |
Abstract: Supervised learning is all about the ability to generalize knowledge. Specifically, the goal of the learning is to train a classifier using training data, in such a way that it will be capable of classifying new unseen data correctly. In order to acheive this goal, it is important to carefully design the lear... |
Title: Exact Enumeration and Sampling of Matrices with Specified Margins |
Abstract: We describe a dynamic programming algorithm for exact counting and exact uniform sampling of matrices with specified row and column sums. The algorithm runs in polynomial time when the column sums are bounded. Binary or non-negative integer matrices are handled. The method is distinguished by applicability to... |
Title: Small-scale inference: Empirical Bayes and confidence methods for as few as a single comparison |
Abstract: By restricting the possible values of the proportion of null hypotheses that are true, the local false discovery rate (LFDR) can be estimated using as few as one comparison. The proportion of proteins with equivalent abundance was estimated to be about 20% for patient group I and about 90% for group II. The s... |
Title: Low-rank Matrix Recovery from Errors and Erasures |
Abstract: This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when ... |
Title: Robust Nonparametric Regression via Sparsity Control with Application to Load Curve Data Cleansing |
Abstract: Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers - that is, da... |
Title: Image Retrieval Method Using Top-surf Descriptor |
Abstract: This report presents the results and details of a content-based image retrieval project using the Top-surf descriptor. The experimental results are preliminary, however, it shows the capability of deducing objects from parts of the objects or from the objects that are similar. This paper uses a dataset consis... |
Title: Visual Concept Detection and Real Time Object Detection |
Abstract: Bag-of-words model is implemented and tried on 10-class visual concept detection problem. The experimental results show that "DURF+ERT+SVM" outperforms "SIFT+ERT+SVM" both in detection performance and computation efficiency. Besides, combining DURF and SIFT results in even better detection performance. Real-t... |
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