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Abstract: Researchers often calculate ratios of measured quantities. Specifying confidence limits for ratios is difficult and the appropriate methods are often unknown. Appropriate methods are described (Fieller, Taylor, special bootstrap methods). For the Fieller method a simple geometrical interpretation is given. Mo... |
Title: An Affinity Propagation Based method for Vector Quantization Codebook Design |
Abstract: In this paper, we firstly modify a parameter in affinity propagation (AP) to improve its convergence ability, and then, we apply it to vector quantization (VQ) codebook design problem. In order to improve the quality of the resulted codebook, we combine the improved AP (IAP) with the conventional LBG algorith... |
Title: Association Rules in the Relational Calculus |
Abstract: One of the most utilized data mining tasks is the search for association rules. Association rules represent significant relationships between items in transactions. We extend the concept of association rule to represent a much broader class of associations, which we refer to as \emphentity-relationship rules.... |
Title: A System for Predicting Subcellular Localization of Yeast Genome Using Neural Network |
Abstract: The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. Many efforts have been made to predict protein subcellul... |
Title: Comparison and Combination of State-of-the-art Techniques for Handwritten Character Recognition: Topping the MNIST Benchmark |
Abstract: Although the recognition of isolated handwritten digits has been a research topic for many years, it continues to be of interest for the research community and for commercial applications. We show that despite the maturity of the field, different approaches still deliver results that vary enough to allow impr... |
Title: The structure of verbal sequences analyzed with unsupervised learning techniques |
Abstract: Data mining allows the exploration of sequences of phenomena, whereas one usually tends to focus on isolated phenomena or on the relation between two phenomena. It offers invaluable tools for theoretical analyses and exploration of the structure of sentences, texts, dialogues, and speech. We report here the r... |
Title: Geometric Analogue of Holographic Reduced Representation |
Abstract: Holographic reduced representations (HRR) are based on superpositions of convolution-bound $n$-tuples, but the $n$-tuples cannot be regarded as vectors since the formalism is basis dependent. This is why HRR cannot be associated with geometric structures. Replacing convolutions by geometric products one arriv... |
Title: Linguistic Information Energy |
Abstract: In this treatment a text is considered to be a series of word impulses which are read at a constant rate. The brain then assembles these units of information into higher units of meaning. A classical systems approach is used to model an initial part of this assembly process. The concepts of linguistic system ... |
Title: Effective linkage learning using low-order statistics and clustering |
Abstract: The adoption of probabilistic models for the best individuals found so far is a powerful approach for evolutionary computation. Increasingly more complex models have been used by estimation of distribution algorithms (EDAs), which often result better effectiveness on finding the global optima for hard optimiz... |
Title: Consistency of trace norm minimization |
Abstract: Regularization by the sum of singular values, also referred to as the trace norm, is a popular technique for estimating low rank rectangular matrices. In this paper, we extend some of the consistency results of the Lasso to provide necessary and sufficient conditions for rank consistency of trace norm minimiz... |
Title: Generating models for temporal representations |
Abstract: We discuss the use of model building for temporal representations. We chose Polish to illustrate our discussion because it has an interesting aspectual system, but the points we wish to make are not language specific. Rather, our goal is to develop theoretical and computational tools for temporal model buildi... |
Title: An efficient reduction of ranking to classification |
Abstract: This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most that of the binary classifier regret, improving a recent result of Balcan et al which only guarantees a factor of 2. Moreover, our ... |
Title: Updating Probabilities: An Econometric Example |
Abstract: We demonstrate how information in the form of observable data and moment constraints are introduced into the method of Maximum relative Entropy (ME). A general example of updating with data and moments is shown. A specific econometric example is solved in detail which can then be used as a template for real w... |
Title: Cinderella - Comparison of INDEpendent RELative Least-squares Amplitudes |
Abstract: The identification of increasingly smaller signal from objects observed with a non-perfect instrument in a noisy environment poses a challenge for a statistically clean data analysis. We want to compute the probability of frequencies determined in various data sets to be related or not, which cannot be answer... |
Title: Using Description Logics for Recognising Textual Entailment |
Abstract: The aim of this paper is to show how we can handle the Recognising Textual Entailment (RTE) task by using Description Logics (DLs). To do this, we propose a representation of natural language semantics in DLs inspired by existing representations in first-order logic. But our most significant contribution is t... |
Title: Probabilistic coherence and proper scoring rules |
Abstract: We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem appears to be new but is closely related to results achieved by other investigators. |
Title: Fuzzy Modeling of Electrical Impedance Tomography Image of the Lungs |
Abstract: Electrical Impedance Tomography (EIT) is a functional imaging method that is being developed for bedside use in critical care medicine. Aiming at improving the chest anatomical resolution of EIT images we developed a fuzzy model based on EIT high temporal resolution and the functional information contained in... |
Title: Nontraditional Scoring of C-tests |
Abstract: In C-tests the hypothesis of items local independence is violated, which doesn't permit to consider them as real tests. It is suggested to determine the distances between separate C-test items (blanks) and to combine items into clusters. Weights, inversely proportional to the number of items in corresponding ... |
Title: Modelling the effects of air pollution on health using Bayesian Dynamic Generalised Linear Models |
Abstract: The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research, in which the standard method of analysis uses Poisson linear or additive models. In this paper we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relat... |
Title: Using Synchronic and Diachronic Relations for Summarizing Multiple Documents Describing Evolving Events |
Abstract: In this paper we present a fresh look at the problem of summarizing evolving events from multiple sources. After a discussion concerning the nature of evolving events we introduce a distinction between linearly and non-linearly evolving events. We present then a general methodology for the automatic creation ... |
Title: Stationary probability density of stochastic search processes in global optimization |
Abstract: A method for the construction of approximate analytical expressions for the stationary marginal densities of general stochastic search processes is proposed. By the marginal densities, regions of the search space that with high probability contain the global optima can be readily defined. The density estimati... |
Title: Bayesian Online Changepoint Detection |
Abstract: Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techn... |
Title: Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical model |
Abstract: Inference for Dirichlet process hierarchical models is typically performed using Markov chain Monte Carlo methods, which can be roughly categorised into marginal and conditional methods. The former integrate out analytically the infinite-dimensional component of the hierarchical model and sample from the marg... |
Title: Analyzing covert social network foundation behind terrorism disaster |
Abstract: This paper addresses a method to analyze the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of no... |
Title: Stability of the Gibbs Sampler for Bayesian Hierarchical Models |
Abstract: We characterise the convergence of the Gibbs sampler which samples from the joint posterior distribution of parameters and missing data in hierarchical linear models with arbitrary symmetric error distributions. We show that the convergence can be uniform, geometric or sub-geometric depending on the relative ... |
Title: Adaptive Importance Sampling in General Mixture Classes |
Abstract: In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of impor... |
Title: Particle Filters for Partially Observed Diffusions |
Abstract: In this paper we introduce a novel particle filter scheme for a class of partially-observed multivariate diffusions. %continuous-time dynamic models where the %signal is given by a multivariate diffusion process. We consider a variety of observation schemes, including diffusion observed with error, observatio... |
Title: Causality and Association: The Statistical and Legal Approaches |
Abstract: This paper discusses different needs and approaches to establishing ``causation'' that are relevant in legal cases involving statistical input based on epidemiological (or more generally observational or population-based) information. We distinguish between three versions of ``cause'': the first involves negl... |
Title: The predictability of letters in written english |
Abstract: We show that the predictability of letters in written English texts depends strongly on their position in the word. The first letters are usually the least easy to predict. This agrees with the intuitive notion that words are well defined subunits in written languages, with much weaker correlations across the... |
Title: Bayesian treed Gaussian process models with an application to computer modeling |
Abstract: Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and... |
Title: A Family of Generalized Beta Distributions for Income |
Abstract: The mathematical properties of a family of generalized beta distribution, including beta-normal, skewed-t, log-F, beta-exponential, beta-Weibull distributions have recently been studied in several publications. This paper applies these distributions to the modeling of the size distribution of income and compu... |
Title: The Use of Unlabeled Data in Predictive Modeling |
Abstract: The incorporation of unlabeled data in regression and classification analysis is an increasing focus of the applied statistics and machine learning literatures, with a number of recent examples demonstrating the potential for unlabeled data to contribute to improved predictive accuracy. The statistical basis ... |
Title: Statistical and Clinical Aspects of Hospital Outcomes Profiling |
Abstract: Hospital profiling involves a comparison of a health care provider's structure, processes of care, or outcomes to a standard, often in the form of a report card. Given the ubiquity of report cards and similar consumer ratings in contemporary American culture, it is notable that these are a relatively recent p... |
Title: A Conversation with Shoutir Kishore Chatterjee |
Abstract: Shoutir Kishore Chatterjee was born in Ranchi, a small hill station in India, on November 6, 1934. He received his B.Sc. in statistics from the Presidency College, Calcutta, in 1954, and M.Sc. and Ph.D. degrees in statistics from the University of Calcutta in 1956 and 1962, respectively. He was appointed a le... |
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