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Clustering terms in the Bayesian network retrieval model: a new approach with two term-layers

Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
2004 Applied Soft Computing  
The retrieval performance of an information retrieval system usually increases when it uses the relationships among the terms contained in a given document collection.  ...  An efficient learning method to capture these relationships, based on term clustering, as well as their use for retrieval purposes, is also shown.  ...  But using current collections, where the number of terms and documents is really very large, running a propagation algorithm, even for a polytree, and taking into account that in interactive Information  ... 
doi:10.1016/j.asoc.2003.11.003 fatcat:i5qazq4cyvg3zmrkxylu7yakqi

The BNR model: foundations and performance of a Bayesian network-based retrieval model

Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
2003 International Journal of Approximate Reasoning  
This paper presents an information retrieval model based on the Bayesian network formalism.  ...  A new inference technique, called propagation + evaluation, has been developed in order to obtain the exact probabilities of relevance in the whole network efficiently.  ...  The size of the first class is usually very small, allowing this situation an application of a fast learning, and subsequently also a fast propagation in retrieval time.  ... 
doi:10.1016/j.ijar.2003.07.011 fatcat:mcx7cbt6ofcurlb6lh6n2qr7ba

Survey on Models and Techniques for Root-Cause Analysis [article]

Marc Solé, Victor Muntés-Mulero, Annie Ibrahim Rana, Giovani Estrada
2017 arXiv   pre-print
Automation and computer intelligence to support complex human decisions becomes essential to manage large and distributed systems in the Cloud and IoT era.  ...  As industry dives into the IoT world and the amount of data generated per year grows at an amazing speed, an important question is how to find appropriate mechanisms to determine root causes that can handle  ...  ACKNOWLEDGMENT The authors would like to thank LeanBigData (FP7-619606) project.  ... 
arXiv:1701.08546v2 fatcat:wqv5vl3ovbe4bjmq32gnni7gg4

Anytime anyspace probabilistic inference

Fabio Tozeto Ramos, Fabio Gagliardi Cozman
2005 International Journal of Approximate Reasoning  
Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices.  ...  The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination  ...  We thank two reviewers who gave important suggestions, and the editor, who oversaw this long process with great patience--particularly when waiting for us to produce the final version.  ... 
doi:10.1016/j.ijar.2004.04.001 fatcat:lrvtscaumvbuhny56scwycpn4e

Improving Analysis Phase in Network Forensics by Using Attack Intention Analysis

Mohammad Rasmi, Khaled E. Al-Qawasmi
2016 International Journal of Security and Its Applications  
This paper aims to show the importance of reconstructing attack intentions in order to improve the analysis phase in network forensics.  ...  In this paper, the attack intention model will be improved to present the motivation behind cyber crimes.  ...  Acknowledgment This research is funded by the Deanship of Research and Graduate Studies in Zarqa University /Jordan.  ... 
doi:10.14257/ijsia.2016.10.5.28 fatcat:6eeledjmlra2ramdkkktpvvc3y

Anytime anyspace probabilistic inference

F RAMOS
2004 International Journal of Approximate Reasoning  
Adaptive conditioning can produce inferences in situations that defy existing algorithms, and is particularly suited as a component of bounded agents and embedded devices.  ...  The main result of our investigation is the adaptive conditioning algorithm, an inference algorithm that works by dividing a Bayesian network into sub-networks and processing each sub-network with a combination  ...  We thank two reviewers who gave important suggestions, and the editor, who oversaw this long process with great patience--particularly when waiting for us to produce the final version.  ... 
doi:10.1016/s0888-613x(04)00047-7 fatcat:yu4uqhddarcodfum3knugvam3i

A Bayesian network-based framework for semantic image understanding

Jiebo Luo, Andreas E. Savakis, Amit Singhal
2005 Pattern Recognition  
The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or  ...  Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships.  ...  In practical applications of BN, it is necessary to employ certain restrictions and assumptions to make the problem of belief propagation in the BN tractable [29, 31, 32] .  ... 
doi:10.1016/j.patcog.2004.11.001 fatcat:zslchdqztnhz5hlekby75vltiu

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation

Roberto Santana, Pedro Larrañaga, José A. Lozano
2010 Evolutionary Computation  
In this paper, we introduce the affinity propagation EDA which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing  ...  algorithm known as affinity propagation.  ...  We introduce affinity propagation in EDAs as an efficient way to find the problem structure from the mutual information matrix.  ... 
doi:10.1162/evco_a_00002 pmid:20583913 fatcat:mcdgabeyozektb5v27wqjiy7ui

Machine Learning, Neural, and Statistical Classification

John F. Elder IV, Donald Michie, David J. Spiegelhalter, Charles C. Taylor
1996 Journal of the American Statistical Association  
In this list we aim to include those who contributed to the Project and the Institutions at which they were primarily based at that time. G. Nakhaeizadeh, J.  ...  Polytrees, are directed graphs which do not contain loops in the skeleton (the network without the arrows) that allow an extremely efficient local propagation procedure.  ...  Like perceptron learning, back-propagation attempts to reduce the errors between the output of the network and the desired result.  ... 
doi:10.2307/2291432 fatcat:mg6mr2lvjnczphrzq4t3iqjoay

Rethinking cognitive architecture via graphical models

Paul S. Rosenbloom
2011 Cognitive Systems Research  
Cognitive architectures need to resolve the diversity dilemma -i.e., to blend diversity and uniformity -in order to couple functionality and efficiency with minimality, integrability, extensibility and  ...  Building diverse architectures upon a uniform implementation level of graphical models is an intriguing approach because of the homogeneous manner in which such models produce state-of-the-art algorithms  ...  Functionally, it achieves one round of parallel associative retrieval of information relevant to the current situation.  ... 
doi:10.1016/j.cogsys.2010.07.006 fatcat:cezcqsjrizcarc2ueqptaz4bju

Index—Volumes 1–89

1997 Artificial Intelligence  
EVAR, extra vehicular activity retriever t 171 exchanger with infinite flow rate 570 exchanges in chess, analysis of -77 exchanging secrets, protocol forexclusion of a point from an interval 1197 exclusive-or  ...  guided -8 hill-climbing -1288 images, ability to -1172 implementation of pn--1066 in a graph, agent -1202 induction as -184 information to direct a -46 iterative-deepening -650, 1066 knowledge  ... 
doi:10.1016/s0004-3702(97)80122-1 fatcat:6az7xycuifaerl7kmv7l3x6rpm

Fundamental Techniques for Geometric and Solid Modeling [chapter]

CHRISTOPH M. HOFFMANN, GEORGE VANĚČEK
1991 Control and Dynamic Systems  
It is an informationally-complete solid representation, and seems to facilitate operations such as automatic mesh generation for finiteelement computations, and geometric tolerancing.  ...  The skeleton is a solid representation originally proposed in computer vision.  ...  ACKNOWLEDGEMENTS 58 ACKNOWLEDGEMENTS We are indebted to H. Pottmann for bringing the work on cyclographic maps to our attention, and to R.  ... 
doi:10.1016/b978-0-12-012748-1.50009-4 fatcat:z2lkxsca2rfavoymg6rbwesmpa

Trends in the development of communication networks: Cognitive networks

Carolina Fortuna, Mihael Mohorcic
2009 Computer Networks  
We discuss their relative merits and identify some future research challenges before we conclude with an overview of standardization efforts.  ...  We start with identifying the most recent research trends in communication networks and classifying them according to the approach taken towards the traditional layered architecture.  ...  Thomas SmartA FOCALE Acknowledgements The authors would like to thank B. Fortuna, J. van der Merwe, S.  ... 
doi:10.1016/j.comnet.2009.01.002 fatcat:kabc6k5spnghhmbbpic4r6zumy

Reusing heterogeneous data for the conceptual design of shapes in virtual environments

Zongcheng Li, Franca Giannini, Jean-Philippe Pernot, Philippe Véron, Bianca Falcidieno
2016 Virtual Reality  
Thus, the definition of new methods able to combine any kind of multimodal data in an easy way would allow non-experts of VE to rapidly mock up objects and scenes.  ...  ., composed by scalable multimodal components) then result from the resolution of a constraint satisfaction problem through an optimization approach.  ...  funded by the European Commission under Grant Agreement 262044, the French National project Co-DIVE and by the Italian National Project ''Tecnologie e sistemi innovativi per la fabbrica del futuro e Made in  ... 
doi:10.1007/s10055-016-0302-z fatcat:p2qzksgzxvflfk32ejpdcu2mfq

Conditioning Graphs: Practical Structures for Inference in Bayesian Networks [chapter]

Kevin Grant, Michael C. Horsch
2005 Lecture Notes in Computer Science  
We also demonstrate several compile ii A graduate degree is not an individual effort. It is the collaboration of many individuals, direct and indirect.  ...  In addition to the conditioning graph architecture, we present several improvements to the model, that maintain its small and simplistic style while reducing the runtime required for computing over it.  ...  In this section, we will revisit this optimization technique, in an effort to reduce the space requirements of caching in conditioning graphs.  ... 
doi:10.1007/11589990_8 fatcat:5kpd6og4czcnxkpkbpipd3fqjq
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