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Title: A note on communicating between information systems based on including degrees |
Abstract: In order to study the communication between information systems, Gong and Xiao [Z. Gong and Z. Xiao, Communicating between information systems based on including degrees, International Journal of General Systems 39 (2010) 189--206] proposed the concept of general relation mappings based on including degrees. ... |
Title: Hyper-g Priors for Generalized Linear Models |
Abstract: We develop an extension of the classical Zellner's g-prior to generalized linear models. The prior on the hyperparameter g is handled in a flexible way, so that any continuous proper hyperprior f(g) can be used, giving rise to a large class of hyper-g priors. Connections with the literature are described in d... |
Title: Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization |
Abstract: The standard training method of Conditional Random Fields (CRFs) is very slow for large-scale applications. As an alternative, piecewise training divides the full graph into pieces, trains them independently, and combines the learned weights at test time. In this paper, we present training for undirected mode... |
Title: Censoring Outdegree Compromises Inferences of Social Network Peer Effects and Autocorrelation |
Abstract: I examine the consequences of modelling contagious influence in a social network with incomplete edge information, namely in the situation where each individual may name a limited number of friends, so that extra outbound ties are censored. In particular, I consider a prototypical time series configuration wh... |
Title: A Learning Algorithm based on High School Teaching Wisdom |
Abstract: A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces... |
Title: Gaussian Process Models for Nonparametric Functional Regression with Functional Responses |
Abstract: Recently nonparametric functional model with functional responses has been proposed within the functional reproducing kernel Hilbert spaces (fRKHS) framework. Motivated by its superior performance and also its limitations, we propose a Gaussian process model whose posterior mode coincide with the fRKHS estima... |
Title: Space and the Synchronic A-Ram |
Abstract: Space is a circuit oriented, spatial programming language designed to exploit the massive parallelism available in a novel formal model of computation called the Synchronic A-Ram, and physically related FPGA and reconfigurable architectures. Space expresses variable grained MIMD parallelism, is modular, stric... |
Title: Biometric Authentication using Nonparametric Methods |
Abstract: The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for a... |
Title: Introduction to the 26th International Conference on Logic Programming Special Issue |
Abstract: This is the preface to the 26th International Conference on Logic Programming Special Issue |
Title: Role of Ontology in Semantic Web Development |
Abstract: World Wide Web (WWW) is the most popular global information sharing and communication system consisting of three standards .i.e., Uniform Resource Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up Language (HTML). Information is provided in text, image, audio and video formats over th... |
Title: Efficient statistical analysis of large correlated multivariate datasets: a case study on brain connectivity matrices |
Abstract: In neuroimaging, a large number of correlated tests are routinely performed to detect active voxels in single-subject experiments or to detect regions that differ between individuals belonging to different groups. In order to bound the probability of a false discovery of pair-wise differences, a Bonferroni or... |
Title: Peak Detection as Multiple Testing |
Abstract: This paper considers the problem of detecting equal-shaped non-overlapping unimodal peaks in the presence of Gaussian ergodic stationary noise, where the number, location and heights of the peaks are unknown. A multiple testing approach is proposed in which, after kernel smoothing, the presence of a peak is t... |
Title: For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia |
Abstract: We report on work in progress on extracting lexical simplifications (e.g., "collaborate" -> "work together"), focusing on utilizing edit histories in Simple English Wikipedia for this task. We consider two main approaches: (1) deriving simplification probabilities via an edit model that accounts for a mixture... |
Title: Discovering shared and individual latent structure in multiple time series |
Abstract: This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Dirici... |
Title: Constraint Propagation for First-Order Logic and Inductive Definitions |
Abstract: Constraint propagation is one of the basic forms of inference in many logic-based reasoning systems. In this paper, we investigate constraint propagation for first-order logic (FO), a suitable language to express a wide variety of constraints. We present an algorithm with polynomial-time data complexity for c... |
Title: Submodular Functions: Learnability, Structure, and Optimization |
Abstract: Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we study submodular functions from a learning theoretic angle. ... |
Title: Separable covariance arrays via the Tucker product, with applications to multivariate relational data |
Abstract: Modern datasets are often in the form of matrices or arrays,potentially having correlations along each set of data indices. For example, data involving repeated measurements of several variables over time may exhibit temporal correlation as well as correlation among the variables. A possible model for matrix-... |
Title: RDFViewS: A Storage Tuning Wizard for RDF Applications |
Abstract: In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a spe... |
Title: Combining spatial information sources while accounting for systematic errors in proxies |
Abstract: Environmental research increasingly uses high-dimensional remote sensing and numerical model output to help fill space-time gaps between traditional observations. Such output is often a noisy proxy for the process of interest. Thus one needs to separate and assess the signal and noise (often called discrepanc... |
Title: Flexible Shrinkage Estimation in High-Dimensional Varying Coefficient Models |
Abstract: We consider the problem of simultaneous variable selection and constant coefficient identification in high-dimensional varying coefficient models based on B-spline basis expansion. Both objectives can be considered as some type of model selection problems and we show that they can be achieved by a double shri... |
Title: Faithfulness in Chain Graphs: The Gaussian Case |
Abstract: This paper deals with chain graphs under the classic Lauritzen-Wermuth-Frydenberg interpretation. We prove that the regular Gaussian distributions that factorize with respect to a chain graph $G$ with $d$ parameters have positive Lebesgue measure with respect to $^d$, whereas those that factorize with respect... |
Title: Epistemic irrelevance in credal nets: the case of imprecise Markov trees |
Abstract: We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing... |
Title: Multigraph Sampling of Online Social Networks |
Abstract: State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation (typically friendship). While powerful, these methods rely on the social graph being fully connected. Furthermore, the mixing time of the sampling process strong... |
Title: Homotopy Perturbation Method for Image Restoration and Denoising |
Abstract: The famous Perona-Malik (P-M) equation which was at first introduced for image restoration has been solved via various numerical methods. In this paper we will solve it for the first time via applying a new numerical method called Homotopy Perturbation Method (HMP) and the correspondent approximated solutions... |
Title: Mining tree-query associations in graphs |
Abstract: New applications of data mining, such as in biology, bioinformatics, or sociology, are faced with large datasetsstructured as graphs. We introduce a novel class of tree-shapedpatterns called tree queries, and present algorithms for miningtree queries and tree-query associations in a large data graph. Novel ab... |
Title: PMOG: The projected mixture of Gaussians model with application to blind source separation |
Abstract: We extend the mixtures of Gaussians (MOG) model to the projected mixture of Gaussians (PMOG) model. In the PMOG model, we assume that q dimensional input data points z_i are projected by a q dimensional vector w into 1-D variables u_i. The projected variables u_i are assumed to follow a 1-D MOG model. In the ... |
Title: Efficient and Robust Estimation for a Class of Generalized Linear Longitudinal Mixed Models |
Abstract: We propose a versatile and computationally efficient estimating equation method for a class of hierarchical multiplicative generalized linear mixed models with additive dispersion components, based on explicit modelling of the covariance structure. The class combines longitudinal and random effects models and... |
Title: A Test for Equality of Distributions in High Dimensions |
Abstract: We present a method which tests whether or not two datasets (one of which could be Monte Carlo generated) might come from the same distribution. Our method works in arbitrarily high dimensions. |
Title: A unifying view for performance measures in multi-class prediction |
Abstract: In the last few years, many different performance measures have been introduced to overcome the weakness of the most natural metric, the Accuracy. Among them, Matthews Correlation Coefficient has recently gained popularity among researchers not only in machine learning but also in several application fields s... |
Title: Learning Functions of Few Arbitrary Linear Parameters in High Dimensions |
Abstract: Let us assume that $f$ is a continuous function defined on the unit ball of $\mathbb R^d$, of the form $f(x) = g (A x)$, where $A$ is a $k \times d$ matrix and $g$ is a function of $k$ variables for $k \ll d$. We are given a budget $m \in \mathbb N$ of possible point evaluations $f(x_i)$, $i=1,...,m$, of $f$,... |
Title: On a class of distributions stable under random summation |
Abstract: We investigate a family of distributions having a property of stability-under-addition, provided that the number $\nu$ of added-up random variables in the random sum is also a random variable. We call the corresponding property a \,$\nu$-stability and investigate the situation with the semigroup generated by ... |
Title: Don't 'have a clue'? Unsupervised co-learning of downward-entailing operators |
Abstract: Researchers in textual entailment have begun to consider inferences involving 'downward-entailing operators', an interesting and important class of lexical items that change the way inferences are made. Recent work proposed a method for learning English downward-entailing operators that requires access to a h... |
Title: Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs |
Abstract: We give a deterministic, polynomial-time algorithm for approximately counting the number of 0,1-solutions to any instance of the knapsack problem. On an instance of length n with total weight W and accuracy parameter eps, our algorithm produces a (1 + eps)-multiplicative approximation in time poly(n,log W,1/e... |
Title: Modeling Spammer Behavior: Na\"ive Bayes vs. Artificial Neural Networks |
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