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Abstract: Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Na\"ive Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics ... |
Title: Maximum entropy models and subjective interestingness: an application to tiles in binary databases |
Abstract: Recent research has highlighted the practical benefits of subjective interestingness measures, which quantify the novelty or unexpectedness of a pattern when contrasted with any prior information of the data miner (Silberschatz and Tuzhilin, 1995; Geng and Hamilton, 2006). A key challenge here is the formaliz... |
Title: A Miniature-Based Image Retrieval System |
Abstract: Due to the rapid development of World Wide Web (WWW) and imaging technology, more and more images are available in the Internet and stored in databases. Searching the related images by the querying image is becoming tedious and difficult. Most of the images on the web are compressed by methods based on discre... |
Title: Log-supermodularity of weight functions and the loading monotonicity of weighted insurance premiums |
Abstract: The paper is motivated by a problem concerning the monotonicity of insurance premiums with respect to their loading parameter: the larger the parameter, the larger the insurance premium is expected to be. This property, usually called loading monotonicity, is satisfied by premiums that appear in the literatur... |
Title: Towards Stratification Learning through Homology Inference |
Abstract: A topological approach to stratification learning is developed for point cloud data drawn from a stratified space. Given such data, our objective is to infer which points belong to the same strata. First we define a multi-scale notion of a stratified space, giving a stratification for each radius level. We th... |
Title: Ultrametric and Generalized Ultrametric in Computational Logic and in Data Analysis |
Abstract: Following a review of metric, ultrametric and generalized ultrametric, we review their application in data analysis. We show how they allow us to explore both geometry and topology of information, starting with measured data. Some themes are then developed based on the use of metric, ultrametric and generaliz... |
Title: Pattern Classification In Symbolic Streams via Semantic Annihilation of Information |
Abstract: We propose a technique for pattern classification in symbolic streams via selective erasure of observed symbols, in cases where the patterns of interest are represented as Probabilistic Finite State Automata (PFSA). We define an additive abelian group for a slightly restricted subset of probabilistic finite s... |
Title: Optimally Training a Cascade Classifier |
Abstract: Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although ... |
Title: Belief Propagation Algorithm for Portfolio Optimization Problems |
Abstract: The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti and M. M\'ezard [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation me... |
Title: Formal-language-theoretic Optimal Path Planning For Accommodation of Amortized Uncertainties and Dynamic Effects |
Abstract: We report a globally-optimal approach to robotic path planning under uncertainty, based on the theory of quantitative measures of formal languages. A significant generalization to the language-measure-theoretic path planning algorithm $\nustar$ is presented that explicitly accounts for average dynamic uncerta... |
Title: Proliferating cell nuclear antigen (PCNA) allows the automatic identification of follicles in microscopic images of human ovarian tissue |
Abstract: Human ovarian reserve is defined by the population of nongrowing follicles (NGFs) in the ovary. Direct estimation of ovarian reserve involves the identification of NGFs in prepared ovarian tissue. Previous studies involving human tissue have used hematoxylin and eosin (HE) stain, with NGF populations estimate... |
Title: Approximate Judgement Aggregation |
Abstract: In this paper we analyze judgement aggregation problems in which a group of agents independently votes on a set of complex propositions that has some interdependency constraint between them(e.g., transitivity when describing preferences). We consider the issue of judgement aggregation from the perspective of ... |
Title: A formalism for causal explanations with an Answer Set Programming translation |
Abstract: We examine the practicality for a user of using Answer Set Programming (ASP) for representing logical formalisms. Our example is a formalism aiming at capturing causal explanations from causal information. We show the naturalness and relative efficiency of this translation job. We are interested in the ease f... |
Title: A Simple CW-SSIM Kernel-based Nearest Neighbor Method for Handwritten Digit Classification |
Abstract: We propose a simple kernel based nearest neighbor approach for handwritten digit classification. The "distance" here is actually a kernel defining the similarity between two images. We carefully study the effects of different number of neighbors and weight schemes and report the results. With only a few neare... |
Title: Kernel induced random survival forests |
Abstract: Kernel Induced Random Survival Forests (KIRSF) is a statistical learning algorithm which aims to improve prediction accuracy for survival data. As in Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each individual in the test set. Prediction error is estimated using Harrell's concor... |
Title: NESVM: a Fast Gradient Method for Support Vector Machines |
Abstract: Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this p... |
Title: Hybrid tractability of soft constraint problems |
Abstract: The constraint satisfaction problem (CSP) is a central generic problem in computer science and artificial intelligence: it provides a common framework for many theoretical problems as well as for many real-life applications. Soft constraint problems are a generalisation of the CSP which allow the user to mode... |
Title: Exact Synchronization for Finite-State Sources |
Abstract: We analyze how an observer synchronizes to the internal state of a finite-state information source, using the epsilon-machine causal representation. Here, we treat the case of exact synchronization, when it is possible for the observer to synchronize completely after a finite number of observations. The more ... |
Title: Variable-width confidence intervals in Gaussian regression and penalized maximum likelihood estimators |
Abstract: Hard thresholding, LASSO , adaptive LASSO and SCAD point estimators have been suggested for use in the linear regression context when most of the components of the regression parameter vector are believed to be zero, a sparsity type of assumption. Potscher and Schneider, 2010, Electronic Journal of Statistics... |
Title: Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images |
Abstract: Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and t... |
Title: Structured sparsity-inducing norms through submodular functions |
Abstract: Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turned into a convex optimization problem by replacing the cardinality function by its convex envelope (... |
Title: Online Learning in Case of Unbounded Losses Using the Follow Perturbed Leader Algorithm |
Abstract: In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We present some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on past losses of the exp... |
Title: Machine Learning Approaches for Modeling Spammer Behavior |
Abstract: Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spa... |
Title: Learning from Profession Knowledge: Application on Knitting |
Abstract: Knowledge Management is a global process in companies. It includes all the processes that allow capitalization, sharing and evolution of the Knowledge Capital of the firm, generally recognized as a critical resource of the organization. Several approaches have been defined to capitalize knowledge but few of t... |
Title: An Influence Diagram-Based Approach for Estimating Staff Training in Software Industry |
Abstract: The successful completion of a software development process depends on the analytical capability and foresightedness of the project manager. For the project manager, the main intriguing task is to manage the risk factors as they adversely influence the completion deadline. One such key risk factor is staff tr... |
Title: Mod\'elisation d'une analyse pragma-linguistique d'un forum de discussion |
Abstract: We present in this paper, a modelling of an expertise in pragmatics. We follow knowledge engineering techniques and observe the expert when he analyses a social discussion forum. Then a number of models are defined. These models emphasises the process followed by the expert and a number of criteria used in hi... |
Title: Threshold estimation based on a P-value framework |
Abstract: We use p-values as a discrepancy criterion for identifying the threshold value at which a regression function takes off from its baseline value -- a problem that is motivated by applications in omics experiments, systems engineering, pharmacological dose-response studies and astronomy. In this paper, we study... |
Title: Machine learning for constraint solver design -- A case study for the alldifferent constraint |
Abstract: Constraint solvers are complex pieces of software which require many design decisions to be made by the implementer based on limited information. These decisions affect the performance of the finished solver significantly. Once a design decision has been made, it cannot easily be reversed, although a differen... |
Title: Distributed solving through model splitting |
Abstract: Constraint problems can be trivially solved in parallel by exploring different branches of the search tree concurrently. Previous approaches have focused on implementing this functionality in the solver, more or less transparently to the user. We propose a new approach, which modifies the constraint model of ... |
Title: Bayes Model Selection with Path Sampling: Factor Models and Other Examples |
Abstract: We prove a theorem justifying the regularity conditions which are needed for Path Sampling in Factor Models. We then show that the remaining ingredient, namely, MCMC for calculating the integrand at each point in the path, may be seriously flawed, leading to wrong estimates of Bayes factors. We provide a new ... |
Title: Structural Solutions to Dynamic Scheduling for Multimedia Transmission in Unknown Wireless Environments |
Abstract: In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process (MDP) that explicitly considers the users' heterogeneous multimedia data ... |
Title: A J-function for inhomogeneous point processes |
Abstract: We propose new summary statistics for intensity-reweighted moment stationary point processes that generalise the well known J-, empty space, and nearest-neighbour distance distribution functions, represent them in terms of generating functionals and conditional intensities, and relate them to the inhomogeneou... |
Title: Switching between Hidden Markov Models using Fixed Share |
Abstract: In prediction with expert advice the goal is to design online prediction algorithms that achieve small regret (additional loss on the whole data) compared to a reference scheme. In the simplest such scheme one compares to the loss of the best expert in hindsight. A more ambitious goal is to split the data int... |
Title: Freezing and Sleeping: Tracking Experts that Learn by Evolving Past Posteriors |
Abstract: A problem posed by Freund is how to efficiently track a small pool of experts out of a much larger set. This problem was solved when Bousquet and Warmuth introduced their mixing past posteriors (MPP) algorithm in 2001. In Freund's problem the experts would normally be considered black boxes. However, in this ... |
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