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Title: Back and Forth Between Rules and SE-Models (Extended Version) |
Abstract: Rules in logic programming encode information about mutual interdependencies between literals that is not captured by any of the commonly used semantics. This information becomes essential as soon as a program needs to be modified or further manipulated. We argue that, in these cases, a program should not be ... |
Title: Relational models for contingency tables |
Abstract: The paper considers general multiplicative models for complete and incomplete contingency tables that generalize log-linear and several other models and are entirely coordinate free. Sufficient conditions of the existence of maximum likelihood estimates under these models are given, and it is shown that the u... |
Title: Deformed Statistics Free Energy Model for Source Separation using Unsupervised Learning |
Abstract: A generalized-statistics variational principle for source separation is formulated by recourse to Tsallis' entropy subjected to the additive duality and employing constraints described by normal averages. The variational principle is amalgamated with Hopfield-like learning rules resulting in an unsupervised l... |
Title: Testing for change in mean of heteroskedastic time series |
Abstract: In this paper we consider a Lagrange Multiplier-type test (LM) to detect change in the mean of time series with heteroskedasticity of unknown form. We derive the limiting distribution under the null, and prove the consistency of the test against the alternative of either an abrupt or smooth changes in the mea... |
Title: Continuous Multiclass Labeling Approaches and Algorithms |
Abstract: We study convex relaxations of the image labeling problem on a continuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the originally combinatorial problem. We focus on two specific relaxations ... |
Title: Reduction of fuzzy automata by means of fuzzy quasi-orders |
Abstract: In our recent paper we have established close relationships between state reduction of a fuzzy recognizer and resolution of a particular system of fuzzy relation equations. In that paper we have also studied reductions by means of those solutions which are fuzzy equivalences. In this paper we will see that in... |
Title: Bisimulations for fuzzy automata |
Abstract: Bisimulations have been widely used in many areas of computer science to model equivalence between various systems, and to reduce the number of states of these systems, whereas uniform fuzzy relations have recently been introduced as a means to model the fuzzy equivalence between elements of two possible diff... |
Title: Efficient regularized isotonic regression with application to gene--gene interaction search |
Abstract: Isotonic regression is a nonparametric approach for fitting monotonic models to data that has been widely studied from both theoretical and practical perspectives. However, this approach encounters computational and statistical overfitting issues in higher dimensions. To address both concerns, we present an a... |
Title: Probabilistic analysis of the human transcriptome with side information |
Abstract: Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened u... |
Title: Markov chain Monte Carlo for exact inference for diffusions |
Abstract: We develop exact Markov chain Monte Carlo methods for discretely-sampled, directly and indirectly observed diffusions. The qualification "exact" refers to the fact that the invariant and limiting distribution of the Markov chains is the posterior distribution of the parameters free of any discretisation error... |
Title: Instant Replay: Investigating statistical Analysis in Sports |
Abstract: Technology has had an unquestionable impact on the way people watch sports. Along with this technological evolution has come a higher standard to ensure a good viewing experience for the casual sports fan. It can be argued that the pervasion of statistical analysis in sports serves to satiate the fan's desire... |
Title: Decision Making Agent Searching for Markov Models in Near-Deterministic World |
Abstract: Reinforcement learning has solid foundations, but becomes inefficient in partially observed (non-Markovian) environments. Thus, a learning agent -born with a representation and a policy- might wish to investigate to what extent the Markov property holds. We propose a learning architecture that utilizes combin... |
Title: Low Complexity Kolmogorov-Smirnov Modulation Classification |
Abstract: Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness of fit, is applied for automatic modulation classification (AMC) in this paper. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal... |
Title: Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms |
Abstract: With the explosion of the size of digital dataset, the limiting factor for decomposition algorithms is the over the input, as the input is often stored out-of-core or even off-site. Moreover, we're only interested in algorithms that operate in w.r.t. to the input size, so that arbitrarily large input can be p... |
Title: Practical inventory routing: A problem definition and an optimization method |
Abstract: The global objective of this work is to provide practical optimization methods to companies involved in inventory routing problems, taking into account this new type of data. Also, companies are sometimes not able to deal with changing plans every period and would like to adopt regular structures for serving ... |
Title: A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising |
Abstract: Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a co... |
Title: A covariance regression model |
Abstract: Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanator... |
Title: Named Entity Recognition Using Web Document Corpus |
Abstract: This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at iden... |
Title: Neyman-Pearson classification, convexity and stochastic constraints |
Abstract: Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following proper... |
Title: A generic trust framework for large-scale open systems using machine learning |
Abstract: In many large scale distributed systems and on the web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable environment. A traditio... |
Title: Multi-label Learning via Structured Decomposition and Group Sparsity |
Abstract: In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of ... |
Title: Automatic Detection of Ringworm using Local Binary Pattern (LBP) |
Abstract: In this paper we present a novel approach for automatic recognition of ring worm skin disease based on LBP (Local Binary Pattern) feature extracted from the affected skin images. The proposed method is evaluated by extensive experiments on the skin images collected from internet. The dataset is tested using t... |
Title: Fuzzy Approach to Critical Bus Ranking under Normal and Line Outage Contingencies |
Abstract: Identification of critical or weak buses for a given operating condition is an important task in the load dispatch centre. It has become more vital in view of the threat of voltage instability leading to voltage collapse. This paper presents a fuzzy approach for ranking critical buses in a power system under ... |
Title: Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network |
Abstract: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabet... |
Title: Natural Language Processing (almost) from Scratch |
Abstract: We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering ... |
Title: Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization |
Abstract: We investigate the learning rate of multiple kernel leaning (MKL) with elastic-net regularization, which consists of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for controlling the smoothness. We focus on a sparse setting where the total number of kernels is large but the num... |
Title: An Algorithm for Repairing Low-Quality Video Enhancement Techniques Based on Trained Filter |
Abstract: Multifarious image enhancement algorithms have been used in different applications. Still, some algorithms or modules are imperfect for practical use. When the image enhancement modules have been fixed or combined by a series of algorithms, we need to repair them as a whole part without changing the inside. T... |
Title: Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions |
Abstract: The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by incorporating information about the gradient of the logarithm of the target density. In this paper we study the efficiency of MALA on a natural class of target measures supported on an infinite dimensional Hil... |
Title: Learning transformed product distributions |
Abstract: We consider the problem of learning an unknown product distribution $X$ over $\0,1\^n$ using samples $f(X)$ where $f$ is a transformation function. Each choice of a transformation function $f$ specifies a learning problem in this framework. Information-theoretic arguments show that for every transformation fu... |
Title: Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function |
Abstract: We propose a new approach to the theoretical analysis of Loopy Belief Propagation (LBP) and the Bethe free energy (BFE) by establishing a formula to connect LBP and BFE with a graph zeta function. The proposed approach is applicable to a wide class of models including multinomial and Gaussian types. The conne... |
Title: Application of Mathematical Optimization Procedures to Intervention Effects in Structural Equation Models |
Abstract: For a given statistical model, it often happens that it is necessary to intervene the model to reduce the variances of the output variables. In structural equation models, this can be done by changing the values of the path coefficients by intervention. First, we explain that the expectations and variance mat... |
Title: An Agent Based Architecture (Using Planning) for Dynamic and Semantic Web Services Composition in an EBXML Context |
Abstract: The process-based semantic composition of Web Services is gaining a considerable momentum as an approach for the effective integration of distributed, heterogeneous, and autonomous applications. To compose Web Services semantically, we need an ontology. There are several ways of inserting semantics in Web Ser... |
Title: A Wiki for Business Rules in Open Vocabulary, Executable English |
Abstract: The problem of business-IT alignment is of widespread economic concern. As one way of addressing the problem, this paper describes an online system that functions as a kind of Wiki -- one that supports the collaborative writing and running of business and scientific applications, as rules in open vocabulary, ... |
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