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Abstract: We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on score functions derived from nearest neighbor graphs on $n$-point nominal data. Anomalies are declared whenever the score of a test sample falls below $\alpha$, which is supposed to be the desired false a... |
Title: Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem |
Abstract: Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. We believe that a vital direction in this field must be algorithms that model the activity of genomic parasites, such as transposons, in biological evolution. This publ... |
Title: On the characterization of the regions of feasible trajectories in the workspace of parallel manipulators |
Abstract: It was shown recently that parallel manipulators with several inverse kinematic solutions have the ability to avoid parallel singularities [Chablat 1998a] and self-collisions [Chablat 1998b] by choosing appropriate joint configurations for the legs. In effect, depending on the joint configurations of the legs... |
Title: Distinguishing Cause and Effect via Second Order Exponential Models |
Abstract: We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order expo... |
Title: Word Sense Disambiguation Using English-Spanish Aligned Phrases over Comparable Corpora |
Abstract: In this paper we describe a WSD experiment based on bilingual English-Spanish comparable corpora in which individual noun phrases have been identified and aligned with their respective counterparts in the other language. The evaluation of the experiment has been carried out against SemCor. We show that, with ... |
Title: Which graphical models are difficult to learn? |
Abstract: We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show tha... |
Title: Calibration of 3-d.o.f. Translational Parallel Manipulators Using Leg Observations |
Abstract: The paper proposes a novel approach for the geometrical model calibration of quasi-isotropic parallel kinematic mechanisms of the Orthoglide family. It is based on the observations of the manipulator leg parallelism during motions between the specific test postures and employs a low-cost measuring system comp... |
Title: Estimation of safety areas for epidemic spread |
Abstract: In this work we study safety areas in epidemic spred. The aim of this work is, given the evolution of epidemic at time $t$, find a safety set at time $t+h$. This is, a random set $K_t+h$ such that the probability that infection reaches $K_t+h$ at time $t+h$ is small. More precisely, inspired on the study of e... |
Title: Metric and Kernel Learning using a Linear Transformation |
Abstract: Metric and kernel learning are important in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new d... |
Title: Local likelihood estimation of local parameters for nonstationary random fields |
Abstract: We develop a weighted local likelihood estimate for the parameters that govern the local spatial dependency of a locally stationary random field. The advantage of this local likelihood estimate is that it smoothly downweights the influence of far away observations, works for irregular sampling locations, and ... |
Title: Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity |
Abstract: The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain st... |
Title: D-optimal designs via a cocktail algorithm |
Abstract: A fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs. This "cocktail algorithm" extends the well-known vertex direction method (VDM; Fedorov 1972) and the multiplicative algorithm (Silvey, Titterington and Torsney, 1978), and shares their simplicity and monotonic conve... |
Title: Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II |
Abstract: We prove a central limit theorem for a general class of adaptive Markov Chain Monte Carlo algorithms driven by sub-geometrically ergodic Markov kernels. We discuss in detail the special case of stochastic approximation. We use the result to analyze the asymptotic behavior of an adaptive version of the Metropo... |
Title: A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification |
Abstract: In this paper, we prove a crucial theorem called Mirroring Theorem which affirms that given a collection of samples with enough information in it such that it can be classified into classes and subclasses then (i) There exists a mapping which classifies and subclassifies these samples (ii) There exists a hier... |
Title: Particle filtering within adaptive Metropolis Hastings sampling |
Abstract: We show that it is feasible to carry out exact Bayesian inference for non-Gaussian state space models using an adaptive Metropolis Hastings sampling scheme with the likelihood approximated by the particle filter. Furthermore, an adapyive independent Metropolis Hastings sampler based on a mixture of normals pr... |
Title: Causal Inference on Discrete Data using Additive Noise Models |
Abstract: Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest ... |
Title: Probability matrices, non-negative rank, and parameterizations of mixture models |
Abstract: In this paper we parameterize non-negative matrices of sum one and rank at most two. More precisely, we give a family of parameterizations using the least possible number of parameters. We also show how these parameterizations relate to a class of statistical models, known in Probability and Statistics as mix... |
Title: Feature-Weighted Linear Stacking |
Abstract: Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the gre... |
Title: Strange Bedfellows: Quantum Mechanics and Data Mining |
Abstract: Last year, in 2008, I gave a talk titled \it Quantum Calisthenics. This year I am going to tell you about how the work I described then has spun off into a most unlikely direction. What I am going to talk about is how one maps the problem of finding clusters in a given data set into a problem in quantum mecha... |
Title: An Optimal Method For Wake Detection In SAR Images Using Radon Transformation Combined With Wavelet Filters |
Abstract: A new fangled method for ship wake detection in synthetic aperture radar (SAR) images is explored here. Most of the detection procedure applies the Radon transform as its properties outfit more than any other transformation for the detection purpose. But still it holds problems when the transform is applied t... |
Title: Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting |
Abstract: This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense s... |
Title: Breast Cancer Detection Using Multilevel Thresholding |
Abstract: This paper presents an algorithm which aims to assist the radiologist in identifying breast cancer at its earlier stages. It combines several image processing techniques like image negative, thresholding and segmentation techniques for detection of tumor in mammograms. The algorithm is verified by using mammo... |
Title: Slow Learners are Fast |
Abstract: Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequential in their design which prevents them from taking advantage of modern multi-core architectures. In this paper we prove that online... |
Title: An Innovative Scheme For Effectual Fingerprint Data Compression Using Bezier Curve Representations |
Abstract: Naturally, with the mounting application of biometric systems, there arises a difficulty in storing and handling those acquired biometric data. Fingerprint recognition has been recognized as one of the most mature and established technique among all the biometrics systems. In recent times, with fingerprint re... |
Title: Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound? |
Abstract: The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (sma... |
Title: Bayes estimators for phylogenetic reconstruction |
Abstract: Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet most reconstruction methods like ML do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution. Given tree samples, we propose finding the tree estimate w... |
Title: Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System |
Abstract: This Generalized Discriminant Analysis (GDA) has provided an extremely powerful approach to extracting non linear features. The network traffic data provided for the design of intrusion detection system always are large with ineffective information, thus we need to remove the worthless information from the or... |
Title: A New Computational Schema for Euphonic Conjunctions in Sanskrit Processing |
Abstract: Automated language processing is central to the drive to enable facilitated referencing of increasingly available Sanskrit E texts. The first step towards processing Sanskrit text involves the handling of Sanskrit compound words that are an integral part of Sanskrit texts. This firstly necessitates the proces... |
Title: ANN-based Innovative Segmentation Method for Handwritten text in Assamese |
Abstract: Artificial Neural Network (ANN) s has widely been used for recognition of optically scanned character, which partially emulates human thinking in the domain of the Artificial Intelligence. But prior to recognition, it is necessary to segment the character from the text to sentences, words etc. Segmentation of... |
Title: Imputation Estimators Partially Correct for Model Misspecification |
Abstract: Inference problems with incomplete observations often aim at estimating population properties of unobserved quantities. One simple way to accomplish this estimation is to impute the unobserved quantities of interest at the individual level and then take an empirical average of the imputed values. We show that... |
Title: Comments on "Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein |
Abstract: This is the compilation of our comments submitted to the Journal of the Royal Statistical Society, Series B, to be published within the discussion of the Read Paper of Andrieu, Doucet and Hollenstein. |
Title: Examples as Interaction: On Humans Teaching a Computer to Play a Game |
Abstract: This paper reviews an experiment in human-computer interaction, where interaction takes place when humans attempt to teach a computer to play a strategy board game. We show that while individually learned models can be shown to improve the playing performance of the computer, their straightforward composition... |
Title: Irregular sets and Central Limit Theorems for dependent triangular arrays |
Abstract: In previous papers, we studied the asymptotic behaviour of $S_N(A,X)=(2N+1)^-d/2\sum_n \in A_N X_n,$ where $X$ is a centered, stationary and weakly dependent random field, and $A_N=A \cap [-N,N]^d$, $A \subset ^d$. This leads to the definition of asymptotically measurable sets, which enjoy the property that $... |
Title: Identification and quantification of Granger causality between gene sets |
Abstract: Wiener and Granger have introduced an intuitive concept of causality between two variables which is based on the idea that an effect never occurs before its cause. Later, Geweke has generalized this concept to a multivariate Granger causality, i.e., n variables Granger-cause another variable. Although Granger... |
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