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Title: Experiment Study of Entropy Convergence of Ant Colony Optimization |
Abstract: Ant colony optimization (ACO) has been applied to the field of combinatorial optimization widely. But the study of convergence theory of ACO is rare under general condition. In this paper, the authors try to find the evidence to prove that entropy is related to the convergence of ACO, especially to the estima... |
Title: Termination Prediction for General Logic Programs |
Abstract: We present a heuristic framework for attacking the undecidable termination problem of logic programs, as an alternative to current termination/non-termination proof approaches. We introduce an idea of termination prediction, which predicts termination of a logic program in case that neither a termination nor ... |
Title: Estimation for a Partial-Linear Single-Index Model |
Abstract: In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimation procedure is proposed to estimate the link function for the single index and the parameters in the single index, as well as the parameters in the linear component of the model. Asymptotic normality is establ... |
Title: Experience-driven formation of parts-based representations in a model of layered visual memory |
Abstract: Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. T... |
Title: A more robust boosting algorithm |
Abstract: We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm. |
Title: Dimension reduction and variable selection in case control studies via regularized likelihood optimization |
Abstract: Dimension reduction and variable selection are performed routinely in case-control studies, but the literature on the theoretical aspects of the resulting estimates is scarce. We bring our contribution to this literature by studying estimators obtained via L1 penalized likelihood optimization. We show that th... |
Title: Non-Bayesian particle filters |
Abstract: Particle filters for data assimilation in nonlinear problems use "particles" (replicas of the underlying system) to generate a sequence of probability density functions (pdfs) through a Bayesian process. This can be expensive because a significant number of particles has to be used to maintain accuracy. We of... |
Title: Percolation Thresholds of Updated Posteriors for Tracking Causal Markov Processes in Complex Networks |
Abstract: Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. S... |
Title: Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models |
Abstract: Estimating the innovation probability density is an important issue in any regression analysis. This paper focuses on functional autoregressive models. A residual-based kernel estimator is proposed for the innovation density. Asymptotic properties of this estimator depend on the average prediction error of th... |
Title: Combining Supervised and Unsupervised Learning for GIS Classification |
Abstract: This paper presents a new hybrid learning algorithm for unsupervised classification tasks. We combined Fuzzy c-means learning algorithm and a supervised version of Minimerror to develop a hybrid incremental strategy allowing unsupervised classifications. We applied this new approach to a real-world database i... |
Title: Quantified Multimodal Logics in Simple Type Theory |
Abstract: We present a straightforward embedding of quantified multimodal logic in simple type theory and prove its soundness and completeness. Modal operators are replaced by quantification over a type of possible worlds. We present simple experiments, using existing higher-order theorem provers, to demonstrate that t... |
Title: On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods |
Abstract: We present a case-study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop compu... |
Title: The Role of Self-Forensics in Vehicle Crash Investigations and Event Reconstruction |
Abstract: This paper further introduces and formalizes a novel concept of self-forensics for automotive vehicles, specified in the Forensic Lucid language. We argue that self-forensics, with the forensics taken out of the cybercrime domain, is applicable to "self-dissection" of intelligent vehicles and hardware systems... |
Title: On Design and Implementation of the Distributed Modular Audio Recognition Framework: Requirements and Specification Design Document |
Abstract: We present the requirements and design specification of the open-source Distributed Modular Audio Recognition Framework (DMARF), a distributed extension of MARF. The distributed version aggregates a number of distributed technologies (e.g. Java RMI, CORBA, Web Services) in a pluggable and modular model along ... |
Title: Generalized Kernel-based Visual Tracking |
Abstract: In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a ... |
Title: On the Workings of Genetic Algorithms: The Genoclique Fixing Hypothesis |
Abstract: We recently reported that the simple genetic algorithm (SGA) is capable of performing a remarkable form of sublinear computation which has a straightforward connection with the general problem of interacting attributes in data-mining. In this paper we explain how the SGA can leverage this computational profic... |
Title: A sticky HDP-HMM with application to speaker diarization |
Abstract: We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in... |
Title: Point-Set Registration: Coherent Point Drift |
Abstract: Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown non-rigid spatial transformation, l... |
Title: Information-theoretic limits of selecting binary graphical models in high dimensions |
Abstract: The problem of graphical model selection is to correctly estimate the graph structure of a Markov random field given samples from the underlying distribution. We analyze the information-theoretic limitations of the problem of graph selection for binary Markov random fields under high-dimensional scaling, in w... |
Title: Monotonic convergence of a general algorithm for computing optimal designs |
Abstract: Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. Theory Methods 14 (1978) 1379--1389] for computing optimal designs. A conjecture of Titterington [Appl. Stat. 27 (1978) 227--234] is confirmed as a consequence. ... |
Title: Designing a Bayesian Network for Preventive Maintenance from Expert Opinions in a Rapid and Reliable Way |
Abstract: In this study, a Bayesian Network (BN) is considered to represent a nuclear plant mechanical system degradation. It describes a causal representation of the phenomena involved in the degradation process. Inference from such a BN needs to specify a great number of marginal and conditional probabilities. As, in... |
Title: Do not Choose Representation just Change: An Experimental Study in States based EA |
Abstract: Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be ve... |
Title: Colorization of Natural Images via L1 Optimization |
Abstract: Natural images in the colour space YUV have been observed to have a non-Gaussian, heavy tailed distribution (called 'sparse') when the filter G(U)(r) = U(r) - sum_s \in N(r) w(Y)_rs U(s), is applied to the chromacity channel U (and equivalently to V), where w is a weighting function constructed from the inten... |
Title: A statistical learning approach to color demosaicing |
Abstract: A statistical learning/inference framework for color demosaicing is presented. We start with simplistic assumptions about color constancy, and recast color demosaicing as a blind linear inverse problem: color parameterizes the unknown kernel, while brightness takes on the role of a latent variable. An expecta... |
Title: Extreme deconvolution: Inferring complete distribution functions from noisy, heterogeneous and incomplete observations |
Abstract: We generalize the well-known mixtures of Gaussians approach to density estimation and the accompanying Expectation--Maximization technique for finding the maximum likelihood parameters of the mixture to the case where each data point carries an individual $d$-dimensional uncertainty covariance and has unique ... |
Title: Automatic Summarization System coupled with a Question-Answering System (QAAS) |
Abstract: To select the most relevant sentences of a document, it uses an optimal decision algorithm that combines several metrics. The metrics processes, weighting and extract pertinence sentences by statistical and informational algorithms. This technique might improve a Question-Answering system, whose function is t... |
Title: Average-Case Active Learning with Costs |
Abstract: We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have different costs. Moreover, queries may have more than two possible responses and the distribution over hypotheses may be non uniform. Specific applic... |
Title: Adaptive inference for the mean of a stochastic process in functional data |
Abstract: This paper proposes and analyzes fully data driven methods for inference about the mean function of a stochastic process from a sample of independent trajectories of the process, observed at discrete time points and corrupted by additive random error. The proposed method uses thresholded least squares estimat... |
Title: A Note on the Complexity of the Satisfiability Problem for Graded Modal Logics |
Abstract: Graded modal logic is the formal language obtained from ordinary (propositional) modal logic by endowing its modal operators with cardinality constraints. Under the familiar possible-worlds semantics, these augmented modal operators receive interpretations such as "It is true at no fewer than 15 accessible wo... |
Title: Skellam shrinkage: Wavelet-based intensity estimation for inhomogeneous Poisson data |
Abstract: The ubiquity of integrating detectors in imaging and other applications implies that a variety of real-world data are well modeled as Poisson random variables whose means are in turn proportional to an underlying vector-valued signal of interest. In this article, we first show how the so-called Skellam distri... |
Title: An Object-Oriented and Fast Lexicon for Semantic Generation |
Abstract: This paper is about the technical design of a large computational lexicon, its storage, and its access from a Prolog environment. Traditionally, efficient access and storage of data structures is implemented by a relational database management system. In Delilah, a lexicon-based NLP system, efficient access t... |
Title: Information Distance in Multiples |
Abstract: Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering, and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minim... |
Title: Learning Nonlinear Dynamic Models |
Abstract: We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory showing this new approach is consistent for models with long range structure, and apply the approach to motion capture and high-dimen... |
Title: Interpretations of the Web of Data |
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