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Title: Hierarchical multilinear models for multiway data |
Abstract: Reduced-rank decompositions provide descriptions of the variation among the elements of a matrix or array. In such decompositions, the elements of an array are expressed as products of low-dimensional latent factors. This article presents a model-based version of such a decomposition, extending the scope of r... |
Title: Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine |
Abstract: Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM re... |
Title: Detection of Bleeding in Wireless Capsule Endoscopy Images Using Range Ratio Color |
Abstract: Wireless Capsule Endoscopy (WCE) is device to detect abnormalities in colon,esophagus,small intestinal and stomach, to distinguish bleeding in WCE images from non bleeding is a hard job by human reviewing and very time consuming. Consequently, automation for classifying bleeding frames not only will expedite ... |
Title: Failover in cellular automata |
Abstract: A cellular automata (CA) configuration is constructed that exhibits emergent failover. The configuration is based on standard Game of Life rules. Gliders and glider-guns form the core messaging structure in the configuration. The blinker is represented as the basic computational unit, and it is shown how it c... |
Title: On the clustering aspect of nonnegative matrix factorization |
Abstract: This paper provides a theoretical explanation on the clustering aspect of nonnegative matrix factorization (NMF). We prove that even without imposing orthogonality nor sparsity constraint on the basis and/or coefficient matrix, NMF still can give clustering results, thus providing a theoretical support for ma... |
Title: Quantitative parametrization of texts written by Ivan Franko: An attempt of the project |
Abstract: In the article, the project of quantitative parametrization of all texts by Ivan Franko is manifested. It can be made only by using modern computer techniques after the frequency dictionaries for all Franko's works are compiled. The paper describes the application spheres, methodology, stages, principles and ... |
Title: Model Selection Principles in Misspecified Models |
Abstract: Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information cr... |
Title: Semiparametric regression in testicular germ cell data |
Abstract: It is possible to approach regression analysis with random covariates from a semiparametric perspective where information is combined from multiple multivariate sources. The approach assumes a semiparametric density ratio model where multivariate distributions are "regressed" on a reference distribution. A ke... |
Title: Empirical learning aided by weak domain knowledge in the form of feature importance |
Abstract: Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowledge while being cost effective. Weak knowledge in the form of feature relative importance (FRI) is presented and explained. Feature... |
Title: Multi-View Active Learning in the Non-Realizable Case |
Abstract: The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prov... |
Title: A generic tool to generate a lexicon for NLP from Lexicon-Grammar tables |
Abstract: Lexicon-Grammar tables constitute a large-coverage syntactic lexicon but they cannot be directly used in Natural Language Processing (NLP) applications because they sometimes rely on implicit information. In this paper, we introduce LGExtract, a generic tool for generating a syntactic lexicon for NLP from the... |
Title: On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem |
Abstract: A sequence $x_1,\dots,x_n,\dots$ of discrete-valued observations is generated according to some unknown probabilistic law (measure) $\mu$. After observing each outcome, one is required to give conditional probabilities of the next observation. The realizable case is when the measure $\mu$ belongs to an arbitr... |
Title: Computing the confidence levels for a root-mean-square test of goodness-of-fit |
Abstract: The classic chi-squared statistic for testing goodness-of-fit has long been a cornerstone of modern statistical practice. The statistic consists of a sum in which each summand involves division by the probability associated with the corresponding bin in the distribution being tested for goodness-of-fit. Typic... |
Title: Ivan Franko's novel Dlja domashnjoho ohnyshcha (For the Hearth) in the light of the frequency dictionary |
Abstract: In the article, the methodology and the principles of the compilation of the Frequency dictionary for Ivan Franko's novel Dlja domashnjoho ohnyshcha (For the Hearth) are described. The following statistical parameters of the novel vocabulary are obtained: variety, exclusiveness, concentration indexes, correla... |
Title: Inferring Networks of Diffusion and Influence |
Abstract: Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very... |
Title: Learning Probabilistic Hierarchical Task Networks to Capture User Preferences |
Abstract: We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributio... |
Title: M\'etodos para la Selecci\'on y el Ajuste de Caracter\'isticas en el Problema de la Detecci\'on de Spam |
Abstract: The email is used daily by millions of people to communicate around the globe and it is a mission-critical application for many businesses. Over the last decade, unsolicited bulk email has become a major problem for email users. An overwhelming amount of spam is flowing into users' mailboxes daily. In 2004, a... |
Title: Information theoretic model validation for clustering |
Abstract: Model selection in clustering requires (i) to specify a suitable clustering principle and (ii) to control the model order complexity by choosing an appropriate number of clusters depending on the noise level in the data. We advocate an information theoretic perspective where the uncertainty in the measurement... |
Title: Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity |
Abstract: Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of utility functions U(u,A), where u is a vector of parameters or task descriptors, maximize or minimize U with respect to u, using networks (Option Nets) which input A and learn to generate good options u stochastically. This paper disc... |
Title: Prediction with Advice of Unknown Number of Experts |
Abstract: In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the NormalHedge bound, which mainly depends on the effective number of experts and also weakly depends on the... |
Title: On Particle Learning |
Abstract: This document is the aggregation of six discussions of Lopes et al. (2010) that we submitted to the proceedings of the Ninth Valencia Meeting, held in Benidorm, Spain, on June 3-8, 2010, in conjunction with Hedibert Lopes' talk at this meeting, and of a further discussion of the rejoinder by Lopes et al. (201... |
Title: A generalized Multiple-try Metropolis version of the Reversible Jump algorithm |
Abstract: The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm is based on drawing several proposals at each step and randomly choosing one of them on ... |
Title: Why Gabor Frames? Two Fundamental Measures of Coherence and Their Role in Model Selection |
Abstract: This paper studies non-asymptotic model selection for the general case of arbitrary design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence---termed ... |
Title: General Purpose Convolution Algorithm in S4-Classes by means of FFT |
Abstract: Object orientation provides a flexible framework for the implementation of the convolution of arbitrary distributions of real-valued random variables. We discuss an algorithm which is based on the discrete Fourier transformation (DFT) and its fast computability via the fast Fourier transformation (FFT). It di... |
Title: Reconstruction of Causal Networks by Set Covering |
Abstract: We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely lo... |
Title: Slice sampling covariance hyperparameters of latent Gaussian models |
Abstract: The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters. Integrating over these hyperparameters considers different possible explanations for the data... |
Title: Variational Program Inference |
Abstract: We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a "solution" to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the mode... |
Title: Computational Tools for Evaluating Phylogenetic and Hierarchical Clustering Trees |
Abstract: Inferential summaries of tree estimates are useful in the setting of evolutionary biology, where phylogenetic trees have been built from DNA data since the 1960's. In bioinformatics, psychometrics and data mining, hierarchical clustering techniques output the same mathematical objects, and practitioners have ... |
Title: Chi-square-based scoring function for categorization of MEDLINE citations |
Abstract: Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our ... |
Title: Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data |
Abstract: Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regressi... |
Title: Tree-Structured Stick Breaking Processes for Hierarchical Data |
Abstract: Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely ... |
Title: The Dilated Triple |
Abstract: The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, c... |
Title: Predictive PAC learnability: a paradigm for learning from exchangeable input data |
Abstract: Exchangeable random variables form an important and well-studied generalization of i.i.d. variables, however simple examples show that no nontrivial concept or function classes are PAC learnable under general exchangeable data inputs $X_1,X_2,\ldots$. Inspired by the work of Berti and Rigo on a Glivenko--Cant... |
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