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Title: A Bernstein-type inequality for stochastic processes of quadratic forms of Gaussian variables |
Abstract: We introduce a Bernstein-type inequality which serves to uniformly control quadratic forms of gaussian variables. The latter can for example be used to derive sharp model selection criteria for linear estimation in linear regression and linear inverse problems via penalization, and we do not exclude that its ... |
Title: Extension of Path Probability Method to Approximate Inference over Time |
Abstract: There has been a tremendous growth in publicly available digital video footage over the past decade. This has necessitated the development of new techniques in computer vision geared towards efficient analysis, storage and retrieval of such data. Many mid-level computer vision tasks such as segmentation, obje... |
Title: Randomized Algorithms for Large scale SVMs |
Abstract: We propose a randomized algorithm for training Support vector machines(SVMs) on large datasets. By using ideas from Random projections we show that the combinatorial dimension of SVMs is $O(log n)$ with high probability. This estimate of combinatorial dimension is used to derive an iterative algorithm, called... |
Title: Random scattering of bits by prediction |
Abstract: We investigate a population of binary mistake sequences that result from learning with parametric models of different order. We obtain estimates of their error, algorithmic complexity and divergence from a purely random Bernoulli sequence. We study the relationship of these variables to the learner's informat... |
Title: FDR control with adaptive procedures and FDR monotonicity |
Abstract: The steep rise in availability and usage of high-throughput technologies in biology brought with it a clear need for methods to control the False Discovery Rate (FDR) in multiple tests. Benjamini and Hochberg (BH) introduced in 1995 a simple procedure and proved that it provided a bound on the expected value,... |
Title: Kinematic calibration of Orthoglide-type mechanisms from observation of parallel leg motions |
Abstract: The paper proposes a new calibration method for parallel manipulators that allows efficient identification of the joint offsets using observations of the manipulator leg parallelism with respect to the base surface. The method employs a simple and low-cost measuring system, which evaluates deviation of the le... |
Title: A Method for Extraction and Recognition of Isolated License Plate Characters |
Abstract: A method to extract and recognize isolated characters in license plates is proposed. In extraction stage, the proposed method detects isolated characters by using Difference-of-Gaussian (DOG) function, The DOG function, similar to Laplacian of Gaussian function, was proven to produce the most stable image fea... |
Title: Stiffness Analysis Of Multi-Chain Parallel Robotic Systems |
Abstract: The paper presents a new stiffness modelling method for multi-chain parallel robotic manipulators with flexible links and compliant actuating joints. In contrast to other works, the method involves a FEA-based link stiffness evaluation and employs a new solution strategy of the kinetostatic equations, which a... |
Title: Maximum Entropy Estimation for Survey sampling |
Abstract: Calibration methods have been widely studied in survey sampling over the last decades. Viewing calibration as an inverse problem, we extend the calibration technique by using a maximum entropy method. Finding the optimal weights is achieved by considering random weights and looking for a discrete distribution... |
Title: Efficient Calculation of P-value and Power for Quadratic Form Statistics in Multilocus Association Testing |
Abstract: We address the asymptotic and approximate distributions of a large class of test statistics with quadratic forms used in association studies. The statistics of interest do not necessarily follow a chi-square distribution and take the general form $D=X^T A X$, where $X$ follows the multivariate normal distribu... |
Title: Efficient Simulation of a Bivariate Exponential Conditionals Distribution |
Abstract: The bivariate distribution with exponential conditionals (BEC) is introduced by Arnold and Strauss [Bivariate distributions with exponential conditionals, J. Amer. Statist. Assoc. 83 (1988) 522--527]. This work presents a simple and fast algorithm for simulating random variates from this density. |
Title: Towards Multimodal Content Representation |
Abstract: Multimodal interfaces, combining the use of speech, graphics, gestures, and facial expressions in input and output, promise to provide new possibilities to deal with information in more effective and efficient ways, supporting for instance: - the understanding of possibly imprecise, partial or ambiguous multi... |
Title: Rumors in a Network: Who's the Culprit? |
Abstract: We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with a variant of the popular SIR model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term . We establish... |
Title: The meta book and size-dependent properties of written language |
Abstract: Evidence is given for a systematic text-length dependence of the power-law index gamma of a single book. The estimated gamma values are consistent with a monotonic decrease from 2 to 1 with increasing length of a text. A direct connection to an extended Heap's law is explored. The infinite book limit is, as a... |
Title: Telling cause from effect based on high-dimensional observations |
Abstract: We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if both the covariance matrix of the cause and the structure matrix mapping cause to the effect are independently chosen. The ... |
Title: Initialization Free Graph Based Clustering |
Abstract: This paper proposes an original approach to cluster multi-component data sets, including an estimation of the number of clusters. From the construction of a minimal spanning tree with Prim's algorithm, and the assumption that the vertices are approximately distributed according to a Poisson distribution, the ... |
Title: Manipulation and gender neutrality in stable marriage procedures |
Abstract: The stable marriage problem is a well-known problem of matching men to women so that no man and woman who are not married to each other both prefer each other. Such a problem has a wide variety of practical applications ranging from matching resident doctors to hospitals to matching students to schools. A wel... |
Title: Dealing with incomplete agents' preferences and an uncertain agenda in group decision making via sequential majority voting |
Abstract: We consider multi-agent systems where agents' preferences are aggregated via sequential majority voting: each decision is taken by performing a sequence of pairwise comparisons where each comparison is a weighted majority vote among the agents. Incompleteness in the agents' preferences is common in many real-... |
Title: Elicitation strategies for fuzzy constraint problems with missing preferences: algorithms and experimental studies |
Abstract: Fuzzy constraints are a popular approach to handle preferences and over-constrained problems in scenarios where one needs to be cautious, such as in medical or space applications. We consider here fuzzy constraint problems where some of the preferences may be missing. This models, for example, settings where ... |
Title: Flow-Based Propagators for the SEQUENCE and Related Global Constraints |
Abstract: We propose new filtering algorithms for the SEQUENCE constraint and some extensions of the SEQUENCE constraint based on network flows. We enforce domain consistency on the SEQUENCE constraint in $O(n^2)$ time down a branch of the search tree. This improves upon the best existing domain consistency algorithm b... |
Title: The Weighted CFG Constraint |
Abstract: We introduce the weighted CFG constraint and propose a propagation algorithm that enforces domain consistency in $O(n^3|G|)$ time. We show that this algorithm can be decomposed into a set of primitive arithmetic constraints without hindering propagation. |
Title: Prediction of Ordered Random Effects in a Simple Small Area Model |
Abstract: Prediction of a vector of ordered parameters or part of it arises naturally in the context of Small Area Estimation (SAE). For example, one may want to estimate the parameters associated with the top ten areas, the best or worst area, or a certain percentile. We use a simple SAE model to show that estimation ... |
Title: Discrete MDL Predicts in Total Variation |
Abstract: The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable class of models, MDL predictions are close to the true distribution in a strong sense. The result is completely general. No independence, ergodicity, stationarity, iden... |
Title: Scalable Inference for Latent Dirichlet Allocation |
Abstract: We investigate the problem of learning a topic model - the well-known Latent Dirichlet Allocation - in a distributed manner, using a cluster of C processors and dividing the corpus to be learned equally among them. We propose a simple approximated method that can be tuned, trading speed for accuracy according... |
Title: Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times |
Abstract: New methods and theory have recently been developed to nonparametrically estimate cumulative incidence functions for competing risks survival data subject to current status censoring. In particular, the limiting distribution of the nonparametric maximum likelihood estimator and a simplified "naive estimator" ... |
Title: Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS |
Abstract: This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation. Giving a historic of sessions, it is about proposing a method of supervised learning doubled of a classifier permitting to extract the necessary knowledge in order to id... |
Title: Eignets for function approximation on manifolds |
Abstract: Let $\XX$ be a compact, smooth, connected, Riemannian manifold without boundary, $G:\XX\times\XX\to \RR$ be a kernel. Analogous to a radial basis function network, an eignet is an expression of the form $\sum_j=1^M a_jG(\circ,y_j)$, where $a_j\in\RR$, $y_j\in\XX$, $1\le j\le M$. We describe a deterministic, u... |
Title: SpicyMKL |
Abstract: We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. The proposed SpicyMKL iteratively solves smooth minimization problems. Thus, there is no need of solving SVM, LP, or QP internal... |
Title: On the Scope of the Universal-Algebraic Approach to Constraint Satisfaction |
Abstract: The universal-algebraic approach has proved a powerful tool in the study of the complexity of CSPs. This approach has previously been applied to the study of CSPs with finite or (infinite) omega-categorical templates, and relies on two facts. The first is that in finite or omega-categorical structures A, a re... |
Title: Breaking Generator Symmetry |
Abstract: Dealing with large numbers of symmetries is often problematic. One solution is to focus on just symmetries that generate the symmetry group. Whilst there are special cases where breaking just the symmetries in a generating set is complete, there are also cases where no irredundant generating set eliminates al... |
Title: Bounding the Sensitivity of Polynomial Threshold Functions |
Abstract: We give the first non-trivial upper bounds on the average sensitivity and noise sensitivity of polynomial threshold functions. More specifically, for a Boolean function f on n variables equal to the sign of a real, multivariate polynomial of total degree d we prove 1) The average sensitivity of f is at most O... |
Title: Dirichlet Process Mixtures of Generalized Linear Models |
Abstract: We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regressi... |
Title: Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures |
Abstract: The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure and the parameters of the Gaussian distribution on the learning rate as the number of samples increases is discussed. Specifically,... |
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