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Title: Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets |
Abstract: Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. "Structure" can be understood as symmetry and a range of symmetries are expressed by hierarchy. Such symmetries directly point to invariants, that pinpoint intrinsic properties of the data a... |
Title: Statistically Optimal Strategy Analysis of a Competing Portfolio Market with a Polyvariant Profit Function |
Abstract: A competing market model with a polyvariant profit function that assumes "zeitnot" stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for m... |
Title: Structural Drift: The Population Dynamics of Sequential Learning |
Abstract: We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream teacher and then pass samples from the model to their downstream student. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a sp... |
Title: On the Subspace of Image Gradient Orientations |
Abstract: We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population... |
Title: Bounds smaller than the Fisher information for generalized linear models |
Abstract: In this paper, we propose a parameter space augmentation approach that is based on "intentionally" introducing a pseudo-nuisance parameter into generalized linear models for the purpose of variance reduction. We first consider the parameter whose norm is equal to one. By introducing a pseudo-nuisance paramete... |
Title: Evolving Genes to Balance a Pole |
Abstract: We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified version of an Artificial Regulatory Network proposed a few years ago, and the task could be solved only by finding a proper way of con... |
Title: Engineering Optimisation by Cuckoo Search |
Abstract: A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering desig... |
Title: Robust and Adaptive Algorithms for Online Portfolio Selection |
Abstract: We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and ... |
Title: Observable dynamics and coordinate systems for automotive target tracking |
Abstract: We investigate several coordinate systems and dynamical vector fields for target tracking to be used in driver assistance systems. We show how to express the discrete dynamics of maneuvering target vehicles in arbitrary coordinates starting from the target's and the own (ego) vehicle's assumed dynamical model... |
Title: On the computability of conditional probability |
Abstract: As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a universal computational procedure for probabilistic inference. We investiga... |
Title: Random nonlinear model with missing responses |
Abstract: A nonlinear model with response variable missing at random is studied. In order to improve the coverage accuracy, the empirical likelihood ratio (EL) method is considered. The asymptotic distribution of EL statistic and also of its approximation is $\chi^2$ if the parameters are estimated using least squares(... |
Title: Dynamical issues in interactive representation of physical objects |
Abstract: The quality of a simulator equipped with a haptic interface is given by the dynamical properties of its components: haptic interface, simulator and control system. Some application areas of such kind of simulator like musical synthesis, animation or more general, instrumental art have specific requirements as... |
Title: Community extraction for social networks |
Abstract: Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friendship networks to food webs and gene regulatory networks. Most of the existing community detection methods focus on partitioning th... |
Title: Size and power properties of some tests in the Birnbaum-Saunders regression model |
Abstract: The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order $n^-1/2$ and under a sequence of Pitman alternatives, for the nonnull distribution functions ... |
Title: Simulation-based Regularized Logistic Regression |
Abstract: In this paper, we develop a simulation-based framework for regularized logistic regression, exploiting two novel results for scale mixtures of normals. By carefully choosing a hierarchical model for the likelihood by one type of mixture, and implementing regularization with another, we obtain new MCMC schemes... |
Title: Using machine learning to make constraint solver implementation decisions |
Abstract: Programs to solve so-called constraint problems are complex pieces of software which require many design decisions to be made more or less arbitrarily by the implementer. These decisions affect the performance of the finished solver significantly. Once a design decision has been made, it cannot easily be reve... |
Title: Evolution with Drifting Targets |
Abstract: We consider the question of the stability of evolutionary algorithms to gradual changes, or drift, in the target concept. We define an algorithm to be resistant to drift if, for some inverse polynomial drift rate in the target function, it converges to accuracy 1 -- \epsilon , with polynomial resources, and t... |
Title: Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso |
Abstract: We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task... |
Title: Learning Kernel-Based Halfspaces with the Zero-One Loss |
Abstract: We describe and analyze a new algorithm for agnostically learning kernel-based halfspaces with respect to the loss function. Unlike most previous formulations which rely on surrogate convex loss functions (e.g. hinge-loss in SVM and log-loss in logistic regression), we provide finite time/sample guarantees wi... |
Title: Sensitivity of health-related scales is a non-decreasing function of their classes |
Abstract: In biomedical research the use of discrete scales which describe characteristics of individuals are widely applied for the evaluation of clinical conditions. However, the number of classes (partitions) used in a discrete scale has never been mathematically evaluated against the accuracy of a scale to predict ... |
Title: Some distance bounds of branching processes and their diffusion limits |
Abstract: We compute exact values respectively bounds of "distances" - in the sense of (transforms of) power divergences and relative entropy - between two discrete-time Galton-Watson branching processes with immigration GWI for which the offspring as well as the immigration is arbitrarily Poisson-distributed (leading ... |
Title: Morphonette: a morphological network of French |
Abstract: This paper describes in details the first version of Morphonette, a new French morphological resource and a new radically lexeme-based method of morphological analysis. This research is grounded in a paradigmatic conception of derivational morphology where the morphological structure is a structure of the ent... |
Title: Temporal Link Prediction using Matrix and Tensor Factorizations |
Abstract: The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T+1?... |
Title: Image Segmentation by Using Threshold Techniques |
Abstract: This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for t... |
Title: A Soft Computing Model for Physicians' Decision Process |
Abstract: In this paper the author presents a kind of Soft Computing Technique, mainly an application of fuzzy set theory of Prof. Zadeh [16], on a problem of Medical Experts Systems. The choosen problem is on design of a physician's decision model which can take crisp as well as fuzzy data as input, unlike the traditi... |
Title: Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition |
Abstract: In this paper we present an OCR for Handwritten Devnagari Characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for char... |
Title: Face Synthesis (FASY) System for Generation of a Face Image from Human Description |
Abstract: This paper aims at generating a new face based on the human like description using a new concept. The FASY (FAce SYnthesis) System is a Face Database Retrieval and new Face generation System that is under development. One of its main features is the generation of the requested face when it is not found in the... |
Title: Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition |
Abstract: This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face i... |
Title: Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition |
Abstract: In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input feature vector. To reduce that dimension here we have used eigenspace projection (also called as Principal Component Analysis), whi... |
Title: Bayesian inference for general Gaussian graphical models with application to multivariate lattice data |
Abstract: We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the G-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our ... |
Title: LACBoost and FisherBoost: Optimally Building Cascade Classifiers |
Abstract: Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported... |
Title: Incremental Training of a Detector Using Online Sparse Eigen-decomposition |
Abstract: The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In ad... |
Title: The Complexity of Manipulating $k$-Approval Elections |
Abstract: An important problem in computational social choice theory is the complexity of undesirable behavior among agents, such as control, manipulation, and bribery in election systems. These kinds of voting strategies are often tempting at the individual level but disastrous for the agents as a whole. Creating elec... |
Title: Measures of Variability for Bayesian Network Graphical Structures |
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