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Abstract: The structure of a Bayesian network includes a great deal of information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study its variability, which can be used to compare the performance of different lear... |
Title: Classification of LULC Change Detection using Remotely Sensed Data for Coimbatore City, Tamilnadu, India |
Abstract: Maps are used to describe far-off places . It is an aid for navigation and military strategies. Mapping of the lands are important and the mapping work is based on (i). Natural resource management & development (ii). Information technology ,(iii). Environmental development ,(iv). Facility management and (v). ... |
Title: Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology |
Abstract: In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared... |
Title: Application Of Fuzzy System In Segmentation Of MRI Brain Tumor |
Abstract: Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor stru... |
Title: Distantly Labeling Data for Large Scale Cross-Document Coreference |
Abstract: Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we ... |
Title: On the Estimation of the Heavy-Tail Exponent in Time Series using the Max-Spectrum |
Abstract: This paper addresses the problem of estimating the tail index of distributions with heavy, Pareto-type tails for dependent data, that is of interest in the areas of finance, insurance, environmental monitoring and teletraffic analysis. A novel approach based on the max self-similarity scaling behavior of bloc... |
Title: On the estimation of the extremal index based on scaling and resampling |
Abstract: The extremal index parameter theta characterizes the degree of local dependence in the extremes of a stationary time series and has important applications in a number of areas, such as hydrology, telecommunications, finance and environmental studies. In this study, a novel estimator for theta based on the asy... |
Title: On the choice of parameters in Singular Spectrum Analysis and related subspace-based methods |
Abstract: In the present paper we investigate methods related to both the Singular Spectrum Analysis (SSA) and subspace-based methods in signal processing. We describe common and specific features of these methods and consider different kinds of problems solved by them such as signal reconstruction, forecasting and par... |
Title: The role of the nugget term in the Gaussian process method |
Abstract: The maximum likelihood estimate of the correlation parameter of a Gaussian process with and without of a nugget term is studied in the case of the analysis of deterministic models. |
Title: Genetic algorithms and the art of Zen |
Abstract: In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as the Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance o... |
Title: Evidence Algorithm and System for Automated Deduction: A Retrospective View |
Abstract: A research project aimed at the development of an automated theorem proving system was started in Kiev (Ukraine) in early 1960s. The mastermind of the project, Academician V.Glushkov, baptized it "Evidence Algorithm", EA. The work on the project lasted, off and on, more than 40 years. In the framework of the ... |
Title: Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection |
Abstract: In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well a... |
Title: RIP-Based Near-Oracle Performance Guarantees for Subspace-Pursuit, CoSaMP, and Iterative Hard-Thresholding |
Abstract: This paper presents an average case denoising performance analysis for the Subspace Pursuit (SP), the CoSaMP and the IHT algorithms. This analysis considers the recovery of a noisy signal, with the assumptions that (i) it is corrupted by an additive random white Gaussian noise; and (ii) it has a K-sparse repr... |
Title: Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar |
Abstract: This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnT... |
Title: The Lambek-Grishin calculus is NP-complete |
Abstract: The Lambek-Grishin calculus LG is the symmetric extension of the non-associative Lambek calculus NL. In this paper we prove that the derivability problem for LG is NP-complete. |
Title: Smoothing proximal gradient method for general structured sparse regression |
Abstract: We study the problem of estimating high-dimensional regression models regularized by a structured sparsity-inducing penalty that encodes prior structural information on either the input or output variables. We consider two widely adopted types of penalties of this kind as motivating examples: (1) the general ... |
Title: Sequential Monte Carlo Methods for Option Pricing |
Abstract: In the following paper we provide a review and development of sequential Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte Carlo-based algorithms, that are designed to approximate expectations w.r.t a sequence of related probability measures. These approaches have been used, successfully,... |
Title: Integrating Structured Metadata with Relational Affinity Propagation |
Abstract: Structured and semi-structured data describing entities, taxonomies and ontologies appears in many domains. There is a huge interest in integrating structured information from multiple sources; however integrating structured data to infer complex common structures is a difficult task because the integration m... |
Title: A Formalization of the Turing Test |
Abstract: The paper offers a mathematical formalization of the Turing test. This formalization makes it possible to establish the conditions under which some Turing machine will pass the Turing test and the conditions under which every Turing machine (or every Turing machine of the special class) will fail the Turing t... |
Title: Network analysis of a corpus of undeciphered Indus civilization inscriptions indicates syntactic organization |
Abstract: Archaeological excavations in the sites of the Indus Valley civilization (2500-1900 BCE) in Pakistan and northwestern India have unearthed a large number of artifacts with inscriptions made up of hundreds of distinct signs. To date there is no generally accepted decipherment of these sign sequences and there ... |
Title: Dynamic Motion Modelling for Legged Robots |
Abstract: An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to... |
Title: Bayesian clustering in decomposable graphs |
Abstract: In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis... |
Title: A simple and efficient algorithm for fused lasso signal approximator with convex loss function |
Abstract: We consider the augmented Lagrangian method (ALM) as a solver for the fused lasso signal approximator (FLSA) problem. The ALM is a dual method in which squares of the constraint functions are added as penalties to the Lagrangian. In order to apply this method to FLSA, two types of auxiliary variables are intr... |
Title: Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata |
Abstract: Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal h... |
Title: Proofs, proofs, proofs, and proofs |
Abstract: In logic there is a clear concept of what constitutes a proof and what not. A proof is essentially defined as a finite sequence of formulae which are either axioms or derived by proof rules from formulae earlier in the sequence. Sociologically, however, it is more difficult to say what should constitute a pro... |
Title: On Recursive Edit Distance Kernels with Application to Time Series Classification |
Abstract: This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or ti... |
Title: Wirtinger's Calculus in general Hilbert Spaces |
Abstract: The present report, has been inspired by the need of the author and its colleagues to understand the underlying theory of Wirtinger's Calculus and to further extend it to include the kernel case. The aim of the present manuscript is twofold: a) it endeavors to provide a more rigorous presentation of the relat... |
Title: Compression Rate Method for Empirical Science and Application to Computer Vision |
Abstract: This philosophical paper proposes a modified version of the scientific method, in which large databases are used instead of experimental observations as the necessary empirical ingredient. This change in the source of the empirical data allows the scientific method to be applied to several aspects of physical... |
Title: Ranked bandits in metric spaces: learning optimally diverse rankings over large document collections |
Abstract: Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-to-r... |
Title: A note on target distribution ambiguity of likelihood-free samplers |
Abstract: Methods for Bayesian simulation in the presence of computationally intractable likelihood functions are of growing interest. Termed likelihood-free samplers, standard simulation algorithms such as Markov chain Monte Carlo have been adapted for this setting. In this article, by presenting generalisations of ex... |
Title: Using Soft Constraints To Learn Semantic Models Of Descriptions Of Shapes |
Abstract: The contribution of this paper is to provide a semantic model (using soft constraints) of the words used by web-users to describe objects in a language game; a game in which one user describes a selected object of those composing the scene, and another user has to guess which object has been described. The gi... |
Title: An Empirical Study of the Manipulability of Single Transferable Voting |
Abstract: Voting is a simple mechanism to combine together the preferences of multiple agents. Agents may try to manipulate the result of voting by mis-reporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it is NP-hard to compu... |
Title: Symmetries of Symmetry Breaking Constraints |
Abstract: Symmetry is an important feature of many constraint programs. We show that any problem symmetry acting on a set of symmetry breaking constraints can be used to break symmetry. Different symmetries pick out different solutions in each symmetry class. This simple but powerful idea can be used in a number of dif... |
Title: Using a Kernel Adatron for Object Classification with RCS Data |
Abstract: Rapid identification of object from radar cross section (RCS) signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value... |
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