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Title: Analyse de la rigidit\'e des machines outils 3 axes d'architecture parall\`ele hyperstatique |
Abstract: The paper presents a new stiffness modelling method for overconstrained parallel manipulators, which is applied to 3-d.o.f. translational mechanisms. It is based on a multidimensional lumped-parameter model that replaces the link flexibility by localized 6-d.o.f. virtual springs. In contrast to other works, t... |
Title: Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks |
Abstract: We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient stat... |
Title: Random Forests: some methodological insights |
Abstract: This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks... |
Title: High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence |
Abstract: Given i.i.d. observations of a random vector $X \in ^p$, we study the problem of estimating both its covariance matrix $\Sigma^*$, and its inverse covariance or concentration matrix $\Theta^* = (\Sigma^*)^-1$. We estimate $\Theta^*$ by minimizing an $\ell_1$-penalized log-determinant Bregman divergence; in th... |
Title: Zero-state Markov switching count-data models: an empirical assessment |
Abstract: In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accide... |
Title: Markov switching multinomial logit model: an application to accident injury severities |
Abstract: In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident injury severities. These models assume Markov switching in time between two unobserved states of roadway safety. The states are distinct, in the sense that in different states accident severity... |
Title: Grapham: Graphical Models with Adaptive Random Walk Metropolis Algorithms |
Abstract: Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms inclu... |
Title: Penalized Orthogonal-Components Regression for Large p Small n Data |
Abstract: We propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented ... |
Title: An information-theoretic derivation of min-cut based clustering |
Abstract: Min-cut clustering, based on minimizing one of two heuristic cost-functions proposed by Shi and Malik, has spawned tremendous research, both analytic and algorithmic, in the graph partitioning and image segmentation communities over the last decade. It is however unclear if these heuristics can be derived fro... |
Title: A Spectral Algorithm for Learning Hidden Markov Models |
Abstract: Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computationally hard (under cryptographic assumptions), and practitioners typically resort to search heuristics which suffer from the usual lo... |
Title: Importance Sampling of Word Patterns in DNA and Protein Sequences |
Abstract: Monte Carlo methods can provide accurate p-value estimates of word counting test statistics and are easy to implement. They are especially attractive when an asymptotic theory is absent or when either the search sequence or the word pattern is too short for the application of asymptotic formulae. Naive direct... |
Title: Learning Class-Level Bayes Nets for Relational Data |
Abstract: Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning (SRL) has developed a number of new statistical models for such data. In this paper we focus on learning class-level or first-order de... |
Title: Partial Correlation Estimation by Joint Sparse Regression Models |
Abstract: In this paper, we propose a computationally efficient approach -- space(Sparse PArtial Correlation Estimation)-- for selecting non-zero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regressio... |
Title: Automatic Generation of the Axial Lines of Urban Environments to Capture What We Perceive |
Abstract: Based on the concepts of isovists and medial axes, we developed a set of algorithms that can automatically generate axial lines for representing individual linearly stretched parts of open space of an urban environment. Open space is the space between buildings, where people can freely move around. The genera... |
Title: Mapping Images with the Coherence Length Diagrams |
Abstract: Statistical pattern recognition methods based on the Coherence Length Diagram (CLD) have been proposed for medical image analyses, such as quantitative characterisation of human skin textures, and for polarized light microscopy of liquid crystal textures. Further investigations are made on image maps originat... |
Title: Prospective Study for Semantic Inter-Media Fusion in Content-Based Medical Image Retrieval |
Abstract: One important challenge in modern Content-Based Medical Image Retrieval (CBMIR) approaches is represented by the semantic gap, related to the complexity of the medical knowledge. Among the methods that are able to close this gap in CBMIR, the use of medical thesauri/ontologies has interesting perspectives due... |
Title: Kinematic Analysis of a Serial - Parallel Machine Tool: the VERNE machine |
Abstract: The paper derives the inverse and the forward kinematic equations of a serial - parallel 5-axis machine tool: the VERNE machine. This machine is composed of a three-degree-of-freedom (DOF) parallel module and a two-DOF serial tilting table. The parallel module consists of a moving platform that is connected t... |
Title: Indirect Cross-validation for Density Estimation |
Abstract: A new method of bandwidth selection for kernel density estimators is proposed. The method, termed indirect cross-validation, or ICV, makes use of so-called selection kernels. Least squares cross-validation (LSCV) is used to select the bandwidth of a selection-kernel estimator, and this bandwidth is appropriat... |
Title: Empirical study of indirect cross-validation |
Abstract: In this paper we provide insight into the empirical properties of indirect cross-validation (ICV), a new method of bandwidth selection for kernel density estimators. First, we describe the method and report on the theoretical results used to develop a practical-purpose model for certain ICV parameters. Next, ... |
Title: An Integrated Software-based Solution for Modular and Self-independent Networked Robot |
Abstract: An integrated software-based solution for a modular and self-independent networked robot is introduced. The wirelessly operatable robot has been developed mainly for autonomous monitoring works with full control over web. The integrated software solution covers three components : a) the digital signal process... |
Title: Optimal sequential procedures with Bayes decision rules |
Abstract: In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequ... |
Title: Some characterizations of affinely full-dimensional factorial designs |
Abstract: A new class of two-level non-regular fractional factorial designs is defined. We call this class an \it affinely full-dimensional factorial design, meaning that design points in the design of this class are not contained in any affine hyperplane in the vector space over $_2$. The property of the indicator fun... |
Title: A Matlab Implementation of a Flat Norm Motivated Polygonal Edge Matching Method using a Decomposition of Boundary into Four 1-Dimensional Currents |
Abstract: We describe and provide code and examples for a polygonal edge matching method. |
Title: k-means requires exponentially many iterations even in the plane |
Abstract: The k-means algorithm is a well-known method for partitioning n points that lie in the d-dimensional space into k clusters. Its main features are simplicity and speed in practice. Theoretically, however, the best known upper bound on its running time (i.e. O(n^kd)) can be exponential in the number of points. ... |
Title: Approximation Algorithms for Bregman Co-clustering and Tensor Clustering |
Abstract: In the past few years powerful generalizations to the Euclidean k-means problem have been made, such as Bregman clustering [7], co-clustering (i.e., simultaneous clustering of rows and columns of an input matrix) [9,18], and tensor clustering [8,34]. Like k-means, these more general problems also suffer from ... |
Title: Probabilistic reasoning with answer sets |
Abstract: This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for know... |
Title: A Novel Clustering Algorithm Based on Quantum Games |
Abstract: Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and imp... |
Title: Justifications for Logic Programs under Answer Set Semantics |
Abstract: The paper introduces the notion of off-line justification for Answer Set Programming (ASP). Justifications provide a graph-based explanation of the truth value of an atom w.r.t. a given answer set. The paper extends also this notion to provide justification of atoms during the computation of an answer set (on... |
Title: Stroke Fragmentation based on Geometry Features and HMM |
Abstract: Stroke fragmentation is one of the key steps in pen-based interaction. In this letter, we present a unified HMM-based stroke fragmentation technique that can do segment point location and primitive type determination simultaneously. The geometry features included are used to evaluate local features, and the H... |
Title: Elementary epistemological features of machine intelligence |
Abstract: Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of m... |
Title: PDE-Foam - a probability-density estimation method using self-adapting phase-space binning |
Abstract: Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-a... |
Title: Decision trees are PAC-learnable from most product distributions: a smoothed analysis |
Abstract: We consider the problem of PAC-learning decision trees, i.e., learning a decision tree over the n-dimensional hypercube from independent random labeled examples. Despite significant effort, no polynomial-time algorithm is known for learning polynomial-sized decision trees (even trees of any super-constant siz... |
Title: Adaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift |
Abstract: This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results statically and dynamically with those obtained by the Naive Bayes ... |
Title: Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks |
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