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Title: A Medial Axis Based Thinning Strategy for Character Images |
Abstract: Thinning of character images is a big challenge. Removal of strokes or deformities in thinning is a difficult problem. In this paper, we have proposed a medial axis based thinning strategy used for performing skeletonization of printed and handwritten character images. In this method, we have used shape chara... |
Title: Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation |
Abstract: Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative v... |
Title: Happiness is assortative in online social networks |
Abstract: Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results ... |
Title: The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning |
Abstract: We derive an upper bound on the local Rademacher complexity of $\ell_p$-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches aimed at analyzed the case $p=1$ only while our analysis covers all cases $1\leq p\leq\infty$, assuming the differen... |
Title: A note on logistic regression and logistic kernel machine models |
Abstract: This is a note on logistic regression models and logistic kernel machine models. It contains derivations to some of the expressions in a paper -- SNP Set Analysis for Detecting Disease Association Using Exon Sequence Data -- submitted to BMC proceedings by these authors. |
Title: Efficient Multi-Template Learning for Structured Prediction |
Abstract: Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to acco... |
Title: Multiple Kernel Learning: A Unifying Probabilistic Viewpoint |
Abstract: We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bo... |
Title: Estimating $\beta$-mixing coefficients |
Abstract: The literature on statistical learning for time series assumes the asymptotic independence or ``mixing' of the data-generating process. These mixing assumptions are never tested, nor are there methods for estimating mixing rates from data. We give an estimator for the $\beta$-mixing rate based on a single sta... |
Title: Generalization error bounds for stationary autoregressive models |
Abstract: We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without further regularization. This lets us use structural risk minimization for model selection. We demonstrate our methods by predictin... |
Title: Adapting to Non-stationarity with Growing Expert Ensembles |
Abstract: When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary ... |
Title: Teraflop-scale Incremental Machine Learning |
Abstract: We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together ... |
Title: A Feature Selection Method for Multivariate Performance Measures |
Abstract: Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. B... |
Title: Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints |
Abstract: In the paper we address the problem of finding the most probable state of discrete Markov random field (MRF) with associative pairwise terms. Although of practical importance, this problem is known to be NP-hard in general. We propose a new type of MRF decomposition, submodular decomposition (SMD). Unlike exi... |
Title: Pairwise ranking: choice of method can produce arbitrarily different rank order |
Abstract: We examine three methods for ranking by pairwise comparison: Principal Eigenvector, HodgeRank and Tropical Eigenvector. It is shown that the choice of method can produce arbitrarily different rank order.To be precise, for any two of the three methods, and for any pair of rankings of at least four items, there... |
Title: Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation |
Abstract: In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which... |
Title: GRASP and path-relinking for Coalition Structure Generation |
Abstract: In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition, maximising a social welfare, of a set of entities involved in a system into exhaustive and disjoint coalitions. The solution of the CSG problem finds... |
Title: A Directional Feature with Energy based Offline Signature Verification Network |
Abstract: Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, b... |
Title: Automatic Wrapper Adaptation by Tree Edit Distance Matching |
Abstract: Information distributed through the Web keeps growing faster day by day, and for this reason, several techniques for extracting Web data have been suggested during last years. Often, extraction tasks are performed through so called wrappers, procedures extracting information from Web pages, e.g. implementing ... |
Title: Design of Automatically Adaptable Web Wrappers |
Abstract: Nowadays, the huge amount of information distributed through the Web motivates studying techniques to be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises developed several approaches of Web data extraction, for example using techniques of artificial int... |
Title: Localization from Incomplete Noisy Distance Measurements |
Abstract: We consider the problem of positioning a cloud of points in the Euclidean space $^d$, using noisy measurements of a subset of pairwise distances. This task has applications in various areas, such as sensor network localization and reconstruction of protein conformations from NMR measurements. Also, it is clos... |
Title: Group Lasso for high dimensional sparse quantile regression models |
Abstract: This paper studies the statistical properties of the group Lasso estimator for high dimensional sparse quantile regression models where the number of explanatory variables (or the number of groups of explanatory variables) is possibly much larger than the sample size while the number of variables in "active" ... |
Title: Evaluation of a Novel Approach for Automatic Volume Determination of Glioblastomas Based on Several Manual Expert Segmentations |
Abstract: The glioblastoma multiforme is the most common malignant primary brain tumor and is one of the highest malignant human neoplasms. During the course of disease, the evaluation of tumor volume is an essential part of the clinical follow-up. However, manual segmentation for acquisition of tumor volume is a time-... |
Title: A Semi-Automatic Graph-Based Approach for Determining the Boundary of Eloquent Fiber Bundles in the Human Brain |
Abstract: Diffusion Tensor Imaging (DTI) allows estimating the position, orientation and dimension of bundles of nerve pathways. This non-invasive imaging technique takes advantage of the diffusion of water molecules and determines the diffusion coefficients for every voxel of the data set. The identification of the di... |
Title: Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks |
Abstract: This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing s... |
Title: A Discrete Evolutionary Model for Chess Players' Ratings |
Abstract: The Elo system for rating chess players, also used in other games and sports, was adopted by the World Chess Federation over four decades ago. Although not without controversy, it is accepted as generally reliable and provides a method for assessing players' strengths and ranking them in official tournaments.... |
Title: The tractability of CSP classes defined by forbidden patterns |
Abstract: The constraint satisfaction problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort has been spent on identifying tractable subclasses. The main two approaches consider structural properties (restrictions on the hy... |
Title: All Roads Lead To Rome |
Abstract: This short article presents a class of projection-based solution algorithms to the problem considered in the pioneering work on compressed sensing - perfect reconstruction of a phantom image from 22 radial lines in the frequency domain. Under the framework of projection-based image reconstruction, we will sho... |
Title: On Minimal Constraint Networks |
Abstract: In a minimal binary constraint network, every tuple of a constraint relation can be extended to a solution. The tractability or intractability of computing a solution to such a minimal network was a long standing open question. Dechter conjectured this computation problem to be NP-hard. We prove this conjectu... |
Title: A Gentle Introduction to the Kernel Distance |
Abstract: This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited exposure to technology more common to machine learning, functional analysis and geometric measure theory. The key aspect of the kernel ... |
Title: Information Theoretic Limits on Learning Stochastic Differential Equations |
Abstract: Consider the problem of learning the drift coefficient of a stochastic differential equation from a sample path. In this paper, we assume that the drift is parametrized by a high dimensional vector. We address the question of how long the system needs to be observed in order to learn this vector of parameters... |
Title: Planning Graph Heuristics for Belief Space Search |
Abstract: Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis ... |
Title: A Kernel Approach to Tractable Bayesian Nonparametrics |
Abstract: Inference in popular nonparametric Bayesian models typically relies on sampling or other approximations. This paper presents a general methodology for constructing novel tractable nonparametric Bayesian methods by applying the kernel trick to inference in a parametric Bayesian model. For example, Gaussian pro... |
Title: Aorta Segmentation for Stent Simulation |
Abstract: Simulation of arterial stenting procedures prior to intervention allows for appropriate device selection as well as highlights potential complications. To this end, we present a framework for facilitating virtual aortic stenting from a contrast computer tomography (CT) scan. More specifically, we present a me... |
Title: A smooth ROC curve estimator based on log-concave density estimates |
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