检索结果-内蒙古大学图书馆

Convergence of the Guesstimation Algorithm

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2009年第5期38卷 711-718页

作者： Agapie, Adriana Acad Econ Studies Dept Math Bucharest Romania

Economists attempting to build econometric or forecasting models are frequently restricted due to data scarcity in terms of short time series of data, and also of parameter non constancy and under-specification. In this case, a realistic alternative is often to guess rather than to estimate parameters of such models. An algorithm of repetitive guessing (drawing) parameters from iteratively changing distributions, with the objective of minimizing the squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process, has been recently introduced. Despite obvious advantages, especially when applied for undersized empirical models with a large number of parameters, applications of Repetitive Stochastic Guesstimation have been, so far, limited. This has presumably been caused by the lack of rigorous proof of its convergence. Such proof for a class of linear models, both identifiable (in the economic sense) and not, is provided in this article.

关键词： General linear model Martingale Prior beliefs probabilistic algorithms

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving,publishing of opinion polls

引用

COMPUTERS & SECURITY 2013年 37卷 143-154页

作者： Abdalaal, Ahmed Nergiz, Mehmet Ercan Saygin, Yucel Univ Coll Appl Sci Dept Informat Technol Off A501 Gaza Israel Zirve Univ Dept Comp Engn Off 446 Gaziantep Turkey Sabanci Univ Fac Engn & Nat Sci Off FENS 2081 Istanbul Turkey

Public opinion is the belief or thoughts of the public regarding a particular topic, especially one regarding politics, religion or social issues. Opinions may be sensitive since they may reflect a person's perspective, understanding, particular feelings, way of life, and desires. On one hand, public opinion is often collected through a central server which keeps a user profile for each participant and needs to publish this data for research purposes. On the other hand, such publishing of sensitive information without proper de-identification puts individuals' privacy at risk, thus opinions Must be anonymized prior to publishing. While many anonymization approaches for tabular data with single sensitive attribute have been introduced, the proposed approaches do not readily apply to opinion polls. This is because opinions are generally collected on many issues, thus opinion databases have multiple sensitive attributes. Finding and enforcing anonymization models that work on datasets with multiple sensitive attributes while allowing risk analysis on the publisher side is not a well-studied problem. In this work, we identify the privacy problems regarding public opinions and propose a new probabilistic privacy model MSA-diversity, specifically defined. on datasets with multiple sensitive attributes. We also present a heuristic anonymization technique to enforce MSA-diversity. Experimental results on real data show that our approach clearly outperforms the existing approaches in terms of anonymization accuracy. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Privacy Multiple sensitive attributes Single sensitive attributes Data mining probabilistic algorithms

来源：评论

学校读者我要写书评

暂无评论

Incorporating data from multiple sensors for localizing nodes in mobile ad hoc networks

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2007年第9期6卷 1090-1104页

作者： Huang, Rui Zaruba, Gergely V. Univ Texas Dept Comp Sci & Engn Arlington TX 76019 USA

The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.

关键词： mobile computing position measurement radio position measurement probabilistic algorithms ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Deformation Techniques for Sparse Systems

引用

FOUNDATIONS OF COMPUTATIONAL MATHEMATICS 2009年第1期9卷 1-50页

作者： Jeronimo, Gabriela Matera, Guillermo Solerno, Pablo Waissbein, Ariel Univ Buenos Aires Fac Ciencias Exactas & Nat Dept Matemat RA-1428 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Natl Council Sci & Technol RA-1033 Buenos Aires DF Argentina Univ Nacl Gen Sarmiento Inst Desarrollo Humano RA-1613 Buenos Aires DF Argentina CORE Secur Technol CoreLabs Buenos Aires DF Argentina Inst Tecnol Buenos Aires Buenos Aires DF Argentina

We exhibit a probabilistic symbolic algorithm for solving zero-dimensional sparse systems. Our algorithm combines a symbolic homotopy procedure, based on a flat deformation of a certain morphism of affine varieties, with the polyhedral deformation of Huber and Sturmfels. The complexity of our algorithm is cubic in the size of the combinatorial structure of the input system. This size is mainly represented by the cardinality and mixed volume of Newton polytopes of the input polynomials and an arithmetic analogue of the mixed volume associated to the deformations under consideration.

关键词： Sparse system solving Symbolic homotopy algorithms Polyhedral deformations Mixed volume Non-Archimedean height Puiseux expansions of space curves Newton-Hensel lifting Geometric solutions probabilistic algorithms Complexity

来源：评论

学校读者我要写书评

暂无评论

Incremental PLSI for threshold-resilient online event analysis

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2008年第3期20卷 289-299页

作者： Chou, Tzu-Chuan Chen, Meng Chang Acad Sinica Inst Informat Sci Taipei 115 Taiwan

The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a threshold-resilient online algorithm, called the Incremental probabilistic Latent Semantic Indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the Topic Detection and Tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent similar to 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.

关键词： clustering online event analysis probabilistic algorithms text mining

来源：评论

学校读者我要写书评

暂无评论

Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第4期29卷 743-748页

作者： Marcel, Sebastien Millan, Jose del R. IDIAP Res Inst CH-1920 Martigny Switzerland

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram ( EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.

关键词： emerging technologies electroencephalogram biometry signal processing probabilistic algorithms machine learning

来源：评论

学校读者我要写书评

暂无评论

HIGH-PROBABILITY PARALLEL TRANSITIVE-CLOSURE algorithms

引用

SIAM JOURNAL ON COMPUTING 1991年第1期20卷 100-125页

作者： ULLMAN, JD YANNAKAKIS, M AT&T BELL LABS MURRAY HILLNJ 07974

There is a straightforward algorithm for computing the transitive-closure of an n-node graph in O(log2 n) time on an EREW-PRAM, using n3/log n processors, or indeed with M(n)/log n processors if serial matrix multiplication in M(n) time can be done. This algorithm is within a log factor of optimal in work (processor-time product), for solving the all-pairs transitive-closure problem for dense graphs. However, this algorithm is far from optimal when either (a) the graph is sparse, or (b) we want to solve the single-source transitive-closure problem. It would be ideal to have an NC algorithm for transitive-closure that took about e processors for the single-source problem on a graph with n nodes and e greater-than-or-equal-to n arcs, or about en processors for the all-pairs problem on the same graph. While an algorithm that good cannot be offered, algorithms with the following performance can be offered. (1) For single-source, O(n-epsilon) time with O(en1-2-epsilon) processors, provided e greater-than-or-equal-to n2-3-epsilon, and (2) for all-pairs, O(n-epsilon) time and O(en1-epsilon) processors, provided e greater-than-or-equal-to n2-2-epsilon-1. Each of these claims assumes that 0 < epsilon greater-than-or-equal-to 1/2. Importantly, the algorithms are (only) high-probability algorithms;that is, if they find a path, then a path exists, but they may fail to find a path that exists with probability at most 2-alpha-c, where alpha is some positive constant, and c is a multiplier for the time taken by the algorithm. However, it is shown that incorrect results can be detected, thus putting the algorithm in the "Las Vegas" class. Finally, it is shown how to do "breadth-first-search" with the same performance as can be achieved for single-source transitive closure.

关键词： TRANSITIVE CLOSURE REACHABILITY BREADTH-FIRST SEARCH PARALLEL algorithms probabilistic algorithms

来源：评论

学校读者我要写书评

暂无评论

Dimensionality reduction of clustered data sets

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2008年第3期30卷 535-540页

作者： Sanguinetti, Guido Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalization of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.

关键词： dimensionality reduction clustering discriminant analysis probabilistic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Bayesian Inference-Based Framework for RFID Data Cleansing

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第10期25卷 2177-2191页

作者： Ku, Wei-Shinn Chen, Haiquan Wang, Haixun Sun, Min-Te Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Valdosta State Univ Dept Math & Comp Sci Valdosta GA 31698 USA Microsoft Res Asia Beijing Sigma Ctr Beijing 100190 Peoples R China Natl Cent Univ Dept Comp Sci & Informat Engn Tao Yuan 320 Taiwan

The past few years have witnessed the emergence of an increasing number of applications for tracking and tracing based on radio frequency identification (RFID) technologies. However, raw RFID readings are usually of low quality and may contain numerous anomalies. An ideal solution for RFID data cleansing should address the following issues. First, in many applications, duplicate readings of the same object are very common. The solution should take advantage of the resulting data redundancy for data cleaning. Second, prior knowledge about the environment may help improve data quality, and a desired solution must be able to take into account such knowledge. Third, the solution should take advantage of physical constraints in target applications to elevate the accuracy of data cleansing. There are several existing RFID data cleansing techniques. However, none of them support all the aforementioned features. In this paper, we propose a Bayesian inference-based framework for cleaning RFID raw data. We first design an n-state detection model and formally prove that the three-state model can maximize the system performance. Then, we extend the n-state model to support two-dimensional RFID reader arrays and compute the likelihood efficiently. In addition, we devise a Metropolis-Hastings sampler with constraints, which incorporates constraint management to clean RFID data with high efficiency and accuracy. Moreover, to support real-time object monitoring, we present the streaming Bayesian inference method to cope with real-time RFID data streams. Finally, we evaluate the performance of our solutions through extensive experiments.

关键词： Data cleaning probabilistic algorithms spatiotemporal databases uncertainty

来源：评论

学校读者我要写书评

暂无评论

A generalisation of the Cantor-Zassenhaus algorithm

引用

COMPUTING 1997年第4期59卷 325-330页

作者： Hidber, C Mathematics Department Swiss Federal Institute of Technology CH-8092 Zuerich

We generalise the Cantor-Zassenhaus algorithm for factoring polynomials over finite fields. The generalisation yields a class of factorisation algorithms. We compute their factorisation probability and their least upper bound. Fire then give a simple characterisation of the algorithms reaching the least upper bound. As an example we show the Cantor-Zassenhaus and the Ben-Or algorithms have a factorisation probability equal to the least upper bound.

关键词： factoring polynomials finite fields probabilistic algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：