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Title: Overlapping stochastic block models with application to the French political blogosphere |
Abstract: Complex systems in nature and in society are often represented as networks, describing the rich set of interactions between objects of interest. Many deterministic and probabilistic clustering methods have been developed to analyze such structures. Given a network, almost all of them partition the vertices in... |
Title: Node harvest |
Abstract: When choosing a suitable technique for regression and classification with multivariate predictor variables, one is often faced with a tradeoff between interpretability and high predictive accuracy. To give a classical example, classification and regression trees are easy to understand and interpret. Tree ense... |
Title: Finite element model selection using Particle Swarm Optimization |
Abstract: This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-... |
Title: Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks |
Abstract: In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. For secondary users, sensing costs are incurred as the result of primary users' activity. Furthermore, each secondary user pays the cost of transmissions upon successful bidd... |
Title: State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation |
Abstract: Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particula... |
Title: Positive Semidefinite Metric Learning with Boosting |
Abstract: The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahala... |
Title: Importance sampling methods for Bayesian discrimination between embedded models |
Abstract: This paper surveys some well-established approaches on the approximation of Bayes factors used in Bayesian model choice, mostly as covered in Chen et al. (2000). Our focus here is on methods that are based on importance sampling strategies rather than variable dimension techniques like reversible jump MCMC, i... |
Title: A Stochastic Model for Collaborative Recommendation |
Abstract: Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and... |
Title: Fractional differentiation based image processing |
Abstract: There are many resources useful for processing images, most of them freely available and quite friendly to use. In spite of this abundance of tools, a study of the processing methods is still worthy of efforts. Here, we want to discuss the possibilities arising from the use of fractional differential calculus... |
Title: Maximum entropy Edgeworth estimates of the number of integer points in polytopes |
Abstract: Abstract: The number of points $x=(x_1 ,x_2 ,...x_n)$ that lie in an integer cube $C$ in $R^n$ and satisfy the constraints $\sum_j h_ij(x_j )=s_i ,1\le i\le d$ is approximated by an Edgeworth-corrected Gaussian formula based on the maximum entropy density $p$ on $x \in C$, that satisfies $E\sum_j h_ij(x_j )=s... |
Title: $L_0$ regularized estimation for nonlinear models that have sparse underlying linear structures |
Abstract: We study the estimation of $\beta$ for the nonlinear model $y = f(X\beta) + \epsilon$ when $f$ is a nonlinear transformation that is known, $\beta$ has sparse nonzero coordinates, and the number of observations can be much smaller than that of parameters ($n\ll p$). We show that in order to bound the $L_2$ er... |
Title: Effectiveness and Limitations of Statistical Spam Filters |
Abstract: In this paper we discuss the techniques involved in the design of the famous statistical spam filters that include Naive Bayes, Term Frequency-Inverse Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes Additive Regression Tree. We compare these techniques with each other in terms of acc... |
Title: Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications |
Abstract: Food authenticity studies are concerned with determining if food samples have been correctly labeled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes vari... |
Title: A Component Based Heuristic Search Method with Evolutionary Eliminations |
Abstract: Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decom... |
Title: An Agent Based Classification Model |
Abstract: The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in compu... |
Title: Behavior Subtraction |
Abstract: Background subtraction has been a driving engine for many computer vision and video analytics tasks. Although its many variants exist, they all share the underlying assumption that photometric scene properties are either static or exhibit temporal stationarity. While this works in some applications, the model... |
Title: An Evolutionary Squeaky Wheel Optimisation Approach to Personnel Scheduling |
Abstract: The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the o... |
Title: A Conversation with Murray Rosenblatt |
Abstract: On an exquisite March day in 2006, David Brillinger and Richard Davis sat down with Murray and Ady Rosenblatt at their home in La Jolla, California for an enjoyable day of reminiscences and conversation. Our mentor, Murray Rosenblatt, was born on September 7, 1926 in New York City and attended City College of... |
Title: Efficient Bayesian analysis of multiple changepoint models with dependence across segments |
Abstract: We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that... |
Title: An Evening Spent with Bill van Zwet |
Abstract: Willem Rutger van Zwet was born in Leiden, the Netherlands, on March 31, 1934. He received his high school education at the Gymnasium Haganum in The Hague and obtained his Masters degree in Mathematics at the University of Leiden in 1959. After serving in the army for almost two years, he obtained his Ph.D. a... |
Title: An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots |
Abstract: A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then t... |
Title: An Immune Inspired Approach to Anomaly Detection |
Abstract: The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic... |
Title: An Immune Inspired Network Intrusion Detection System Utilising Correlation Context |
Abstract: Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on having access to a database of known attack signatures which... |
Title: Faster Algorithms for Max-Product Message-Passing |
Abstract: Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm, or loopy belief-propagation. The exact solution to this problem is well known to be exponential in the size of the model's maximal cliques after it is triangulated, while app... |
Title: Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis |
Abstract: Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mamm... |
Title: A Fuzzy Petri Nets Model for Computing With Words |
Abstract: Motivated by Zadeh's paradigm of computing with words rather than numbers, several formal models of computing with words have recently been proposed. These models are based on automata and thus are not well-suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent compu... |
Title: Citation Statistics |
Abstract: This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using ``simple and objective'' methods is increasingly prevalent today. The ``simple and objective'' methods are broadly interpreted as bibliometrics, that is, ci... |
Title: Comment: Bibliometrics in the Context of the UK Research Assessment Exercise |
Abstract: Research funding and reputation in the UK have, for over two decades, been increasingly dependent on a regular peer-review of all UK departments. This is to move to a system more based on bibliometrics. Assessment exercises of this kind influence the behavior of institutions, departments and individuals, and ... |
Title: Comment: Citation Statistics |
Abstract: We discuss the paper "Citation Statistics" by the Joint Committee on Quantitative Assessment of Research [arXiv:0910.3529]. In particular, we focus on a necessary feature of "good" measures for ranking scientific authors: that good measures must able to accurately distinguish between authors. |
Title: Comment: Citation Statistics |
Abstract: Comment on "Citation Statistics" [arXiv:0910.3529] |
Title: Comment: Citation Statistics |
Abstract: Comment on "Citation Statistics" [arXiv:0910.3529] |
Title: Rejoinder: Citation Statistics |
Abstract: Rejoinder to "Citation Statistics" [arXiv:0910.3529] |
Title: On Learning Finite-State Quantum Sources |
Abstract: We examine the complexity of learning the distributions produced by finite-state quantum sources. We show how prior techniques for learning hidden Markov models can be adapted to the quantum generator model to find that the analogous state of affairs holds: information-theoretically, a polynomial number of sa... |
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