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Abstract: Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. We propose a completely data-driven calibration algorithm for this parameter in the least-squares regression framework, without assuming a particular sh... |
Title: Least angle and $\ell_1$ penalized regression: A review |
Abstract: Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. Th... |
Title: New Estimation Procedures for PLS Path Modelling |
Abstract: Given R groups of numerical variables X1, ... XR, we assume that each group is the result of one underlying latent variable, and that all latent variables are bound together through a linear equation system. Moreover, we assume that some explanatory latent variables may interact pairwise in one or more equati... |
Title: Calculations of Sobol indices for the Gaussian process metamodel |
Abstract: Global sensitivity analysis of complex numerical models can be performed by calculating variance-based importance measures of the input variables, such as the Sobol indices. However, these techniques, requiring a large number of model evaluations, are often unacceptable for time expensive computer codes. A we... |
Title: Detecting the overlapping and hierarchical community structure of complex networks |
Abstract: Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in ... |
Title: Learning Balanced Mixtures of Discrete Distributions with Small Sample |
Abstract: We study the problem of partitioning a small sample of $n$ individuals from a mixture of $k$ product distributions over a Boolean cube $\0, 1\^K$ according to their distributions. Each distribution is described by a vector of allele frequencies in $\R^K$. Given two distributions, we use $\gamma$ to denote the... |
Title: Bayesian Nonlinear Principal Component Analysis Using Random Fields |
Abstract: We propose a novel model for nonlinear dimension reduction motivated by the probabilistic formulation of principal component analysis. Nonlinearity is achieved by specifying different transformation matrices at different locations of the latent space and smoothing the transformation using a Markov random fiel... |
Title: Network as a computer: ranking paths to find flows |
Abstract: We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneou... |
Title: A Bayesian reassessment of nearest-neighbour classification |
Abstract: The k-nearest-neighbour procedure is a well-known deterministic method used in supervised classification. This paper proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment made in a previous analysis of this method... |
Title: Les Agents comme des interpr\'eteurs Scheme : Sp\'ecification dynamique par la communication |
Abstract: We proposed in previous papers an extension and an implementation of the STROBE model, which regards the Agents as Scheme interpreters. These Agents are able to interpret messages in a dedicated environment including an interpreter that learns from the current conversation therefore representing evolving meta... |
Title: Extreme Learning Machine for land cover classification |
Abstract: This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require set... |
Title: A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization |
Abstract: We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators from "users" to the "objects" they rate. Recent low-rank type matrix completion approaches to CF are shown to be special cases. However, unlike existing regularization based CF methods, our a... |
Title: On the $\ell_1-\ell_q$ Regularized Regression |
Abstract: In this paper we consider the problem of grouped variable selection in high-dimensional regression using $\ell_1-\ell_q$ regularization ($1\leq q \leq \infty$), which can be viewed as a natural generalization of the $\ell_1-\ell_2$ regularization (the group Lasso). The key condition is that the dimensionality... |
Title: Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model |
Abstract: Estimating probabilistic deformable template models is a new approach in the fields of computer vision and probabilistic atlases in computational anatomy. A first coherent statistical framework modelling the variability as a hidden random variable has been given by Allassonni\`ere, Amit and Trouv\'e in [1] in... |
Title: M-decomposability, elliptical unimodal densities, and applications to clustering and kernel density estimation |
Abstract: Chia and Nakano (2009) introduced the concept of M-decomposability of probability densities in one-dimension. In this paper, we generalize M-decomposability to any dimension. We prove that all elliptical unimodal densities are M-undecomposable. We also derive an inequality to show that it is better to represe... |
Title: Combining Expert Advice Efficiently |
Abstract: We show how models for prediction with expert advice can be defined concisely and clearly using hidden Markov models (HMMs); standard HMM algorithms can then be used to efficiently calculate, among other things, how the expert predictions should be weighted according to the model. We cast many existing models... |
Title: FINE: Fisher Information Non-parametric Embedding |
Abstract: We consider the problems of clustering, classification, and visualization of high-dimensional data when no straightforward Euclidean representation exists. Typically, these tasks are performed by first reducing the high-dimensional data to some lower dimensional Euclidean space, as many manifold learning meth... |
Title: Why stratification may hurt, & how much |
Abstract: There are circumstances under which stratified sampling is worse than simple random sampling, even if the allocation of the sample sizes is optimal. This phenomenon was discovered more than sixty years ago, but is not as widely known as one might expect. We provide it with lower and upper bounds for its badne... |
Title: New Implementation Framework for Saturation-Based Reasoning |
Abstract: The saturation-based reasoning methods are among the most theoretically developed ones and are used by most of the state-of-the-art first-order logic reasoners. In the last decade there was a sharp increase in performance of such systems, which I attribute to the use of advanced calculi and the intensified re... |
Title: Support Vector classifiers for Land Cover Classification |
Abstract: Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and art... |
Title: A New Approach of Point Estimation from Truncated or Grouped and Censored Data |
Abstract: We propose a new approach for estimating the parameters of a probability distribution. It consists on combining two new methods of estimation. The first is based on the definition of a new distance measuring the difference between variations of two distributions on a finite number of points from their support... |
Title: A Radar-Shaped Statistic for Testing and Visualizing Uniformity Properties in Computer Experiments |
Abstract: In the study of computer codes, filling space as uniformly as possible is important to describe the complexity of the investigated phenomenon. However, this property is not conserved by reducing the dimension. Some numeric experiment designs are conceived in this sense as Latin hypercubes or orthogonal arrays... |
Title: Textual Fingerprinting with Texts from Parkin, Bassewitz, and Leander |
Abstract: Current research in author profiling to discover a legal author's fingerprint does not only follow examinations based on statistical parameters only but include more and more dynamic methods that can learn and that react adaptable to the specific behavior of an author. But the question on how to appropriately... |
Title: Compressed Counting |
Abstract: Counting is among the most fundamental operations in computing. For example, counting the pth frequency moment has been a very active area of research, in theoretical computer science, databases, and data mining. When p=1, the task (i.e., counting the sum) can be accomplished using a simple counter. Compresse... |
Title: Higher-Order Properties of Analytic Wavelets |
Abstract: The influence of higher-order wavelet properties on the analytic wavelet transform behavior is investigated, and wavelet functions offering advantageous performance are identified. This is accomplished through detailed investigation of the generalized Morse wavelets, a two-parameter family of exactly analytic... |
Title: Multiclass Approaches for Support Vector Machine Based Land Cover Classification |
Abstract: SVMs were initially developed to perform binary classification; though, applications of binary classification are very limited. Most of the practical applications involve multiclass classification, especially in remote sensing land cover classification. A number of methods have been proposed to implement SVMs... |
Title: Controlled stratification for quantile estimation |
Abstract: In this paper we propose and discuss variance reduction techniques for the estimation of quantiles of the output of a complex model with random input parameters. These techniques are based on the use of a reduced model, such as a metamodel or a response surface. The reduced model can be used as a control vari... |
Title: Sign Language Tutoring Tool |
Abstract: In this project, we have developed a sign language tutor that lets users learn isolated signs by watching recorded videos and by trying the same signs. The system records the user's video and analyses it. If the sign is recognized, both verbal and animated feedback is given to the user. The system is able to ... |
Title: Anisotropic selection in cellular genetic algorithms |
Abstract: In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure... |
Title: A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models |
Abstract: We discuss an efficient implementation of the iterative proportional scaling procedure in the multivariate Gaussian graphical models. We show that the computational cost can be reduced by localization of the update procedure in each iterative step by using the structure of a decomposable model obtained by tri... |
Title: A Markov Basis for Conditional Test of Common Diagonal Effect in Quasi-Independence Model for Square Contingency Tables |
Abstract: In two-way contingency tables we sometimes find that frequencies along the diagonal cells are relatively larger(or smaller) compared to off-diagonal cells, particularly in square tables with the common categories for the rows and the columns. In this case the quasi-independence model with an additional parame... |
Title: The normal distribution in some constrained sample spaces |
Abstract: Phenomena with a constrained sample space appear frequently in practice. This is the case e.g. with strictly positive data and with compositional data, like percentages and the like. If the natural measure of difference is not the absolute one, it is possible to use simple algebraic properties to show that it... |
Title: Pure Exploration for Multi-Armed Bandit Problems |
Abstract: We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. These forecasters are assessed in terms of their simple regret, a regret notion that captures the fact that exploration is only constr... |
Title: Time Varying Undirected Graphs |
Abstract: Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using $\ell_1$ penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves o... |
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