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Title: Dimension-free tail inequalities for sums of random matrices |
Abstract: We derive exponential tail inequalities for sums of random matrices with no dependence on the explicit matrix dimensions. These are similar to the matrix versions of the Chernoff bound and Bernstein inequality except with the explicit matrix dimensions replaced by a trace quantity that can be small even when ... |
Title: Automatic Vehicle Checking Agent (VCA) |
Abstract: A definition of intelligence is given in terms of performance that can be quantitatively measured. In this study, we have presented a conceptual model of Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve this goal, we have introduced several kinds of agents that exhibit intellige... |
Title: A Proposed Decision Support System/Expert System for Guiding Fresh Students in Selecting a Faculty in Gomal University, Pakistan |
Abstract: This paper presents the design and development of a proposed rule based Decision Support System that will help students in selecting the best suitable faculty/major decision while taking admission in Gomal University, Dera Ismail Khan, Pakistan. The basic idea of our approach is to design a model for testing ... |
Title: Slicing: Nonsingular Estimation of High Dimensional Covariance Matrices Using Multiway Kronecker Delta Covariance Structures |
Abstract: Nonsingular estimation of high dimensional covariance matrices is an important step in many statistical procedures like classification, clustering, variable selection an future extraction. After a review of the essential background material, this paper introduces a technique we call slicing for obtaining a no... |
Title: Generalized Isotonic Regression |
Abstract: We present a computational and statistical approach for fitting isotonic models under convex differentiable loss functions. We offer a recursive partitioning algorithm which provably and efficiently solves isotonic regression under any such loss function. Models along the partitioning path are also isotonic a... |
Title: Locally Adaptive Density Estimation on the Unit Sphere Using Needlets |
Abstract: The problem of estimating a probability density function f on the (d-1)-dimensional unit sphere S^d-1 from directional data using the needlet frame is considered. It is shown that the decay of needlet coefficients supported near a point of a function f depends only on local H\"older continuity properties of f... |
Title: Convex and Network Flow Optimization for Structured Sparsity |
Abstract: We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of l_2- or l_infinity-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address h... |
Title: "Improved FCM algorithm for Clustering on Web Usage Mining" |
Abstract: In this paper we present clustering method is very sensitive to the initial center values, requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster... |
Title: Cross-Fertilizing Strategies for Better EM Mountain Climbing and DA Field Exploration: A Graphical Guide Book |
Abstract: In recent years, a variety of extensions and refinements have been developed for data augmentation based model fitting routines. These developments aim to extend the application, improve the speed and/or simplify the implementation of data augmentation methods, such as the deterministic EM algorithm for mode ... |
Title: The EM Algorithm in Genetics, Genomics and Public Health |
Abstract: The popularity of the EM algorithm owes much to the 1977 paper by Dempster, Laird and Rubin. That paper gave the algorithm its name, identified the general form and some key properties of the algorithm and established its broad applicability in scientific research. This review gives a nontechnical introductio... |
Title: Rational Deployment of CSP Heuristics |
Abstract: Heuristics are crucial tools in decreasing search effort in varied fields of AI. In order to be effective, a heuristic must be efficient to compute, as well as provide useful information to the search algorithm. However, some well-known heuristics which do well in reducing backtracking are so heavy that the g... |
Title: Off-Line Handwritten Signature Retrieval using Curvelet Transforms |
Abstract: In this paper, a new method for offline handwritten signature retrieval is based on curvelet transform is proposed. Many applications in image processing require similarity retrieval of an image from a large collection of images. In such cases, image indexing becomes important for efficient organization and r... |
Title: Adaptive Evolutionary Clustering |
Abstract: In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends whil... |
Title: Unified Treatment of Hidden Markov Switching Models |
Abstract: Many real-world problems encountered in several disciplines deal with the modeling of time-series containing different underlying dynamical regimes, for which probabilistic approaches are very often employed. In this paper we describe several such approaches in the common framework of graphical models. We giv... |
Title: Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression |
Abstract: Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in pract... |
Title: Materials to the Russian-Bulgarian Comparative Dictionary "EAD" |
Abstract: This article presents a fragment of a new comparative dictionary "A comparative dictionary of names of expansive action in Russian and Bulgarian languages". Main features of the new web-based comparative dictionary are placed, the principles of its formation are shown, primary links between the word-matches a... |
Title: Modulated Oscillations in Three Dimensions |
Abstract: The analysis of the fully three-dimensional and time-varying polarization characteristics of a modulated trivariate, or three-component, oscillation is addressed. The use of the analytic operator enables the instantaneous three-dimensional polarization state of any square-integrable trivariate signal to be un... |
Title: Template-based matching using weight maps |
Abstract: Template matching is one of the most prevalent pattern recognition methods worldwide. It has found uses in most visual concept detection fields. In this work, we investigate methods for improving template matching by adjusting the weights of different regions of the template. We compare several weight maps an... |
Title: Analysis of Modulated Multivariate Oscillations |
Abstract: The concept of a common modulated oscillation spanning multiple time series is formalized, a method for the recovery of such a signal from potentially noisy observations is proposed, and the time-varying bias properties of the recovery method are derived. The method, an extension of wavelet ridge analysis to ... |
Title: GEOMIR2K9 - A Similar Scene Finder |
Abstract: The main goal of the GEOMIR2K9 project is to create a software program that is able to find similar scenic images clustered by geographical location and sorted by similarity based only on their visual content. The user should be able to input a query image, based on this given query image the program should f... |
Title: A Universal Part-of-Speech Tagset |
Abstract: To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a resu... |
Title: PAC learnability versus VC dimension: a footnote to a basic result of statistical learning |
Abstract: A fundamental result of statistical learnig theory states that a concept class is PAC learnable if and only if it is a uniform Glivenko-Cantelli class if and only if the VC dimension of the class is finite. However, the theorem is only valid under special assumptions of measurability of the class, in which ca... |
Title: Learning Active Basis Models by EM-Type Algorithms |
Abstract: EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is... |
Title: From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree |
Abstract: We demonstrate the possibility of coding parts, features that are higher level than boundaries, using a modified AT field after augmenting the interaction term of the AT energy with a non-local term and weakening the separation into boundary/not-boundary phases. The iteratively extracted parts using the level... |
Title: Extracting Parts of 2D Shapes Using Local and Global Interactions Simultaneously |
Abstract: Perception research provides strong evidence in favor of part based representation of shapes in human visual system. Despite considerable differences among different theories in terms of how part boundaries are found, there is substantial agreement on that the process depends on many local and global geometri... |
Title: The EM Algorithm and the Rise of Computational Biology |
Abstract: In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transform... |
Title: The Discrepancy Principle for Choosing Bandwidths in Kernel Density Estimation |
Abstract: We investigate the discrepancy principle for choosing smoothing parameters for kernel density estimation. The method is based on the distance between the empirical and estimated distribution functions. We prove some new positive and negative results on L_1-consistency of kernel estimators with bandwidths chos... |
Title: The MM Alternative to EM |
Abstract: The EM algorithm is a special case of a more general algorithm called the MM algorithm. Specific MM algorithms often have nothing to do with missing data. The first M step of an MM algorithm creates a surrogate function that is optimized in the second M step. In minimization, MM stands for majorize--minimize;... |
Title: From EM to Data Augmentation: The Emergence of MCMC Bayesian Computation in the 1980s |
Abstract: It was known from Metropolis et al. [J. Chem. Phys. 21 (1953) 1087--1092] that one can sample from a distribution by performing Monte Carlo simulation from a Markov chain whose equilibrium distribution is equal to the target distribution. However, it took several decades before the statistical community embra... |
Title: Elimination of Specular reflection and Identification of ROI: The First Step in Automated Detection of Cervical Cancer using Digital Colposcopy |
Abstract: Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, Specular Reflections (SR) appear as bright spots heavily saturated with white light. These... |
Title: Hybrid Deterministic-Stochastic Methods for Data Fitting |
Abstract: Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms offer inexpensive iterations by sampling a subset of the terms in the sum. These methods can make great progress initially,... |
Title: Block-Conditional Missing at Random Models for Missing Data |
Abstract: Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the data $Z$ and missing data indicators $M$, and associate... |
Title: Parameter Expansion and Efficient Inference |
Abstract: This EM review article focuses on parameter expansion, a simple technique introduced in the PX-EM algorithm to make EM converge faster while maintaining its simplicity and stability. The primary objective concerns the connection between parameter expansion and efficient inference. It reviews the statistical i... |
Title: Appropriate Methodology of Statistical Tests According to Prior Probability and Required Objectivity |
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