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Title: Approximating Data with weighted smoothing Splines |
Abstract: Given a data set (t_i, y_i), i=1,..., n with the t_i in [0,1] non-parametric regression is concerned with the problem of specifying a suitable function f_n:[0,1] -> R such that the data can be reasonably approximated by the points (t_i, f_n(t_i)), i=1,..., n. If a data set exhibits large variations in local b... |
Title: PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers |
Abstract: The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni in the classification setting to more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators. A particular attention is paid to randomized estimators drawn in a smal... |
Title: Hierarchy construction schemes within the Scale set framework |
Abstract: Segmentation algorithms based on an energy minimisation framework often depend on a scale parameter which balances a fit to data and a regularising term. Irregular pyramids are defined as a stack of graphs successively reduced. Within this framework, the scale is often defined implicitly as the height in the ... |
Title: Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology |
Abstract: Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one o... |
Title: Numerical Sensitivity and Efficiency in the Treatment of Epistemic and Aleatory Uncertainty |
Abstract: The treatment of both aleatory and epistemic uncertainty by recent methods often requires an high computational effort. In this abstract, we propose a numerical sampling method allowing to lighten the computational burden of treating the information by means of so-called fuzzy random variables. |
Title: Robustly estimating the flow direction of information in complex physical systems |
Abstract: We propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) has invariance properties that are important for applications in real physical or biological systems: (a) it is st... |
Title: Decomposition During Search for Propagation-Based Constraint Solvers |
Abstract: We describe decomposition during search (DDS), an integration of And/Or tree search into propagation-based constraint solvers. The presented search algorithm dynamically decomposes sub-problems of a constraint satisfaction problem into independent partial problems, avoiding redundant work. The paper discusses... |
Title: A New Theoretic Foundation for Cross-Layer Optimization |
Abstract: Cross-layer optimization solutions have been proposed in recent years to improve the performance of network users operating in a time-varying, error-prone wireless environment. However, these solutions often rely on ad-hoc optimization approaches, which ignore the different environmental dynamics experienced ... |
Title: Variational inference for large-scale models of discrete choice |
Abstract: Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make ... |
Title: Analysis of nonlinear modes of variation for functional data |
Abstract: A set of curves or images of similar shape is an increasingly common functional data set collected in the sciences. Principal Component Analysis (PCA) is the most widely used technique to decompose variation in functional data. However, the linear modes of variation found by PCA are not always interpretable b... |
Title: An Approximation Ratio for Biclustering |
Abstract: The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independ... |
Title: The Banff Challenge: Statistical Detection of a Noisy Signal |
Abstract: Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a signal will be detected. We discuss a simple probability model for this and derive frequentist and noninformative Bayesian procedu... |
Title: Density estimation in linear time |
Abstract: We consider the problem of choosing a density estimate from a set of distributions F, minimizing the L1-distance to an unknown distribution (Devroye, Lugosi 2001). Devroye and Lugosi analyze two algorithms for the problem: Scheffe tournament winner and minimum distance estimate. The Scheffe tournament estimat... |
Title: The source coding game with a cheating switcher |
Abstract: Motivated by the lossy compression of an active-vision video stream, we consider the problem of finding the rate-distortion function of an arbitrarily varying source (AVS) composed of a finite number of subsources with known distributions. Berger's paper `The Source Coding Game', \emphIEEE Trans. Inform. Theo... |
Title: A Class of LULU Operators on Multi-Dimensional Arrays |
Abstract: The LULU operators for sequences are extended to multi-dimensional arrays via the morphological concept of connection in a way which preserves their essential properties, e.g. they are separators and form a four element fully ordered semi-group. The power of the operators is demonstrated by deriving a total v... |
Title: Gibbs Sampling for a Bayesian Hierarchical General Linear Model |
Abstract: We consider a Bayesian hierarchical version of the normal theory general linear model which is practically relevant in the sense that it is general enough to have many applications and it is not straightforward to sample directly from the corresponding posterior distribution. Thus we study a block Gibbs sampl... |
Title: Common knowledge logic in a higher order proof assistant? |
Abstract: This paper presents experiments on common knowledge logic, conducted with the help of the proof assistant Coq. The main feature of common knowledge logic is the eponymous modality that says that a group of agents shares a knowledge about a certain proposition in a inductive way. This modality is specified by ... |
Title: CLAIRLIB Documentation v1.03 |
Abstract: The Clair library is intended to simplify a number of generic tasks in Natural Language Processing (NLP), Information Retrieval (IR), and Network Analysis. Its architecture also allows for external software to be plugged in with very little effort. Functionality native to Clairlib includes Tokenization, Summa... |
Title: Computer- and robot-assisted urological surgery |
Abstract: The author reviews the computer and robotic tools available to urologists to help in diagnosis and technical procedures. The first part concerns the contribution of robotics and presents several systems at various stages of development (laboratory prototypes, systems under validation or marketed systems). The... |
Title: Universal Intelligence: A Definition of Machine Intelligence |
Abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well know... |
Title: Graph kernels between point clouds |
Abstract: Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer vision and graphics. In this paper, we present extensions of graph kern... |
Title: Pattern Recognition System Design with Linear Encoding for Discrete Patterns |
Abstract: In this paper, designs and analyses of compressive recognition systems are discussed, and also a method of establishing a dual connection between designs of good communication codes and designs of recognition systems is presented. Pattern recognition systems based on compressed patterns and compressed sensor ... |
Title: Network Tomography: Identifiability and Fourier Domain Estimation |
Abstract: The statistical problem for network tomography is to infer the distribution of $$, with mutually independent components, from a measurement model $=A$, where $A$ is a given binary matrix representing the routing topology of a network under consideration. The challenge is that the dimension of $$ is much large... |
Title: Improving the Performance of PieceWise Linear Separation Incremental Algorithms for Practical Hardware Implementations |
Abstract: In this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this und... |
Title: Framework and Resources for Natural Language Parser Evaluation |
Abstract: Because of the wide variety of contemporary practices used in the automatic syntactic parsing of natural languages, it has become necessary to analyze and evaluate the strengths and weaknesses of different approaches. This research is all the more necessary because there are currently no genre- and domain-ind... |
Title: Nonparametric estimation for a stochastic volatility model |
Abstract: Consider discrete time observations (X_\ell\delta)_1\leq \ell \leq n+1$ of the process $X$ satisfying $dX_t= dB_t$, with $V_t$ a one-dimensional positive diffusion process independent of the Brownian motion $B$. For both the drift and the diffusion coefficient of the unobserved diffusion $V$, we propose nonpa... |
Title: Convergence properties of the expected improvement algorithm |
Abstract: This paper has been withdrawn from the arXiv. It is now published by Elsevier in the Journal of Statistical Planning and Inference, under the modified title "Convergence properties of the expected improvement algorithm with fixed mean and covariance functions". See http://dx.doi.org/10.1016/j.jspi.2010.04.018... |
Title: Improved Collaborative Filtering Algorithm via Information Transformation |
Abstract: In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthe... |
Title: Tests of Machine Intelligence |
Abstract: Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In... |
Title: A Fast Hierarchical Multilevel Image Segmentation Method using Unbiased Estimators |
Abstract: This paper proposes a novel method for segmentation of images by hierarchical multilevel thresholding. The method is global, agglomerative in nature and disregards pixel locations. It involves the optimization of the ratio of the unbiased estimators of within class to between class variances. We obtain a recu... |
Title: TRUST-TECH based Methods for Optimization and Learning |
Abstract: Many problems that arise in machine learning domain deal with nonlinearity and quite often demand users to obtain global optimal solutions rather than local optimal ones. Optimization problems are inherent in machine learning algorithms and hence many methods in machine learning were inherited from the optimi... |
Title: Simulation of the matrix Bingham-von Mises-Fisher distribution, with applications to multivariate and relational data |
Abstract: Orthonormal matrices play an important role in reduced-rank matrix approximations and the analysis of matrix-valued data. A matrix Bingham-von Mises-Fisher distribution is a probability distribution on the set of orthonormal matrices that includes linear and quadratic terms, and arises as a posterior distribu... |
Title: Probabilistic Visual Secret Sharing Schemes for Gray-scale images and Color images |
Abstract: Visual secrete sharing (VSS) is an encryption technique that utilizes human visual system in the recovering of the secret image and it does not require any complex calculation. Pixel expansion has been a major issue of VSS schemes. A number of probabilistic VSS schemes with minimum pixel expansion have been p... |
Title: Online EM Algorithm for Latent Data Models |
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