Dissertations / Theses on the topic 'Information filtering'
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Chambers, Brian D. "Adaptive Bayesian information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ45945.pdf.
Full textWebster, David Edward. "Realising context-oriented information filtering." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:2724.
Full textYu, Kai. "Statistical Learning Approaches to Information Filtering." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-25120.
Full textDolbear, Catherine. "Personalised information filtering using event causality." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:31e94de4-5dda-4312-968b-d0ef34dea8e2.
Full textShardanand, Upendra. "Social information filtering for music recommendation." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11667.
Full textOlsson, Tomas. "Information Filtering with Collaborative Interface Agents." Thesis, SICS, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22235.
Full textLanquillon, Carsten. "Enhancing text classification to improve information filtering." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963801805.
Full textKhan, Imran. "Personal adaptive web agent for information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq23361.pdf.
Full textSheth, Beerud Dilip. "A learning approach to personalized information filtering." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/37998.
Full textIncludes bibliographical references (leaves 96-100).
by Beerud Dilip Sheth.
M.S.
Akkapeddi, Raghu C. "Grouping annotating and filtering history information in VKB." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/227.
Full textRydberg, Christoffer. "Time Efficiency of Information Retrieval with Geographic Filtering." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172918.
Full textDen här studien addresserar tidseffektiviteten av två större modeller inom informationssökning: ”Extended Boolean Model” (EBM) och ”Vector Space Model” (VSM) . Båda modellerna använder samma typ av viktningsschema, som bygger på ”term frequency–inverse document frequency“ (tf- idf). I VSM rankas varje dokument, utifrån en söksträng, genom en skalärprodukt av dokumentets och söksträngens vektorrepresentationer. I EBM används såkallade ”p-norm score functions” som rankar dokument, inte bara utifrån matchande termer, utan genom att ta hänsyn till de Booleska sammanbindningar som finns mellan sökorden. Utöver detta undersöker studien hur dokument med en geografisk anknytning kan hämtas baserat på positionen och geometrin av den geografiska ytan. Vidare vill vi besvara hur denna geografiska sökning på bästa sätt kan integreras med de två informationssökningmodellerna. Utifrån tidigare forskning dras slutsatsen att det bästa tillvägagångssättet för dokument med endast en geografisk anknytning är att använda ett index baserat på ”Z-Space Filling Curves” (Z-SFC). När dokument hämtas genom Z-SFC-indexet finns det inga garantier att de hämtade dokumenten är relevanta för sökytan. Det är däremot garanterat att endast dessa dokument kan vara relevanta. Vidare är det rankade utdatat från IR-modellerna till en stor fördel för den geografiska sökningen, nämligen att vi kan fokusera på dokument med hög relevans. Detta görs genom att jämföra resultaten från vald IR-modell med resultaten från Z-SFC-indexet och sortera de matchande dokumenten efter relevans. Därefter kan vi iterera över listan och beräkna vilka dokuments geometrier som skär sökningens geometri. Eftersom användaren endast är intresserad av de högst rankade dokumenten kan vi avbryta när vi har tillräckligt många sökresultat. Slutsatsen av studien är att VSM är enkel att implementera och mycket tidseffektiv jämfört med EBM. Modellen är underlägsen EBM i den mening att det är en ganska enkel ”bag of words”-modell, medan EBM tillåter specificering av konjuktioner och disjunktioner. Den geografiska sökningen har visats vara tidseffektiv och oberoende av vilken av de två IR-modellerna som används.Skillnaden i tidseffektivitet mellan VSM och EBM ökar däremot drastiskt när söksträngen blir längre och fler resultat erhålls. Emellertid, beroende på användarens krav, storleken på dokumentsamlingen, söksträngens längd, etc., kan fördelarna med EBM ibland överväga nackdelen av den lägre prestandan. För sökmotorer med stora dokumentsamlingar och många användare är dock modellen sannolikt för långsam.
Tam, Ming-wai, and 譚銘威. "Scalable collaborative filtering using updatable indexing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40687351.
Full textTam, Ming-wai. "Scalable collaborative filtering using updatable indexing." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40687351.
Full textBizer, Christian. "Quality-driven Information Filtering in the Context of web-based Information Systems." [S.l.] : [s.n.], 2007. http://www.diss.fu-berlin.de/2007/217/index.html.
Full textLiang, Winnie H. (Winnie Hui-Ning). "Managing information overload on the Web with collaborative filtering." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/32181.
Full textIncludes bibliographical references (leaves 102-103).
by Winnie H. Liang.
M.Eng.
Maltz, David A. (David Aaron). "Distributing information for collaborative filtering on Usenet net news." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36464.
Full textSriram, Bharath. "Short Text Classification in Twitter to Improve Information Filtering." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275406094.
Full textKapanipathi, Pavan. "Personalized and Adaptive Semantic Information Filtering for Social Media." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464541093.
Full textClaubnitzer, Diana. "Bacterial chemotaxis: sensory adaptation, noise filtering, and information transmission." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/6918.
Full textTong, Shan. "Dynamic physiological information recovery : a sampled-data filtering framework /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20TONG.
Full textNguyen, Tran Diem Hanh. "Semantic-based topic evaluation and application in information filtering." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/209882/1/Tran%20Diem%20Hanh_Nguyen_Thesis.pdf.
Full textGao, Yang. "Pattern-based topic modelling and its application for information filtering and information retrieval." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/83982/1/Yang_Gao_Thesis.pdf.
Full textYang, Li. "Building an Intelligent Filtering System Using Idea Indexing." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4275/.
Full textWidyantoro, Dwi Hendratmo. "Concept drift learning and its application to adaptive information filtering." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/170.
Full textMoukas, Alexandros G. "Amalthaea--information filtering and discovery using a multiagent evolving system." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/62338.
Full textStrunjas, Svetlana. "Algorithms and Models for Collaborative Filtering from Large Information Corpora." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.
Full textMohd, Azmi Nurulhuda Firdaus. "Artificial immune systems for information filtering : focusing on profile adaptation." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/6695/.
Full textBlankenburg, Sven. "Theoretical mechanisms of information filtering in stochastic single neuron models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17577.
Full textNeurons transmit information about time-dependent input signals via highly non-linear responses, so-called action potentials or spikes. This type of information transmission can be frequency-dependent and allows for preferences for certain stimulus components. A single neuron can transmit either slow components (low pass filter), fast components (high pass filter), or intermediate components (band pass filter) of a time-dependent input signal. Using methods developed in theoretical physics (statistical physics) within the framework of information theory, in this thesis, cell-intrinsic mechanisms are being investigated that can lead to frequency selectivity on the level of information transmission. Various stochastic single neuron models are examined numerically and, if tractable analytically. Ranging from simple spiking models to complex conductance-based models with and without nonlinearities, these models include integrator as well as resonator dynamics. First, spectral information filtering characteristics of different types of stochastic current-based integrator neuron models are being studied. Subsequently, the simple deterministic PIF model is being extended with a stochastic spiking rule, leading to positive correlations between successive interspike intervals (ISIs). Thereafter, models are being examined which show subthreshold resonances (so-called resonator models) and their effects on the spectral information filtering characteristics are being investigated. Finally, the spectral information filtering properties of stochastic linearnonlinear cascade neuron models are being researched by employing different static nonlinearities (SNLs). The trade-off between frequency-dependent signal transmission and the total amount of transmitted information will be demonstrated in all models and constitutes a direct consequence of the nonlinear formulation of the models.
Zhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/29350/1/Xujuan_Zhou_Thesis.pdf.
Full textZhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Queensland University of Technology, 2008. http://eprints.qut.edu.au/29350/.
Full textBikdash, Marwan. "Analysis and filtering of time-varying signals." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80015.
Full textMaster of Science
Turner, Brett Ronald. "An investigation into the efficacy of URL content filtering systems." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2409.
Full textLau, Raymond Yiu Keung. "Belief revision for adaptive information agents." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/15789/1/Raymond_Lau_Thesis.pdf.
Full textLau, Raymond Yiu Keung. "Belief Revision for Adaptive Information Agents." Queensland University of Technology, 2003. http://eprints.qut.edu.au/15789/.
Full textFletcher, Douglas Dwayne. "Adaptive filtering for extracting asymmetric rotating body information from measurement sensors." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/15661.
Full textThompson, Gordon A. (Gordon Alexander). "Inertial measurement unit calibration using Full Information Maximum Likelihood Optimal Filtering." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34136.
Full textIncludes bibliographical references (p. 105-108).
The robustness of Full Information Maximum Likelihood Optimal Filtering (FIMLOF) for inertial measurement unit (IMU) calibration in high-g centrifuge environments is considered. FIMLOF uses an approximate Newton's Method to identify Kalman Filter parameters such as process and measurement noise intensities. Normally, IMU process noise intensities and measurement standard deviations are determined by laboratory testing in a 1-g field. In this thesis, they are identified along with the calibration of the IMU during centrifuge testing. The partial derivatives of the Kalman Filter equations necessary to identify these parameters are developed. Using synthetic measurements, the sensitivity of FIMLOF to initial parameter estimates and filter suboptimality is investigated. The filter residuals, the FIMLOF parameters, and their associated statistics are examined. The results show that FIMLOF can be very successful at tuning suboptimal filter models. For systems with significant mismodeling, FIMLOF can substantially improve the IMU calibration and subsequent navigation performance. In addition, FIMLOF can be used to detect mismodeling in a system, through disparities between the laboratory-derived parameter estimates and the FIMLOF parameter estimates.
by Gordon A. Thompson.
S.M.
Almosallam, Ibrahim Ahmad Shang Yi. "A new adaptive framework for collaborative filtering prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5630.
Full textThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 22, 2008) Includes bibliographical references.
De, la Rouviere Simon. "Effectiveness of user-curated filtering as coping strategy for information overload on microblogging services." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86215.
Full textENGLISH ABSTRACT: We are living in an increasingly global and connected society with information creation increasing at exponential rates. The research sets out to help solve the problem of mitigating the effects of information overload in order to increase the novelty of our interactions in the digital age. Online social-networks and microblogging services allow people across the world to take part in a public conversation. These tools have inherent constraints on how much communication can feasibly occur. Become too connected and a user will receive too much information to reasonably process. On Twitter (a microblogging service), lists are a tool for users to create separate feeds. The research determines whether lists are an effective tool for coping with information overload (abundance of updates). Using models of sustainable online discourse and information overload on computer-mediated communication tools, the research found that lists are an effective tool to cope with information overload on microblogging services. Quantitatively, individuals who make use of lists follow more users and when they start using lists they increase the amount of information resources (following other users) at a greater rate than those who do not use lists. Qualitatively, the research also provides insight into the reasons why people use lists. The research adds new academic relevance to ‘information overload’ and ‘online sustainability’ models previously not used in the context of feed-based online CMC tools, and deepens the understanding and importance of usercurated filtering as a way to reap the benefits from the increasing abundance of information in the digital age.
AFRIKAANSE OPSOMMING: Ons leef in ’n toenemend globale en gekonnekteerde samelewing waarin inligtingskepping toeneem teen ’n eksponensiële koers. Hierdie navorsing het ten doel om die newe-effekte van die oorvloed van inligting te verlig sodat daar meer waarde uit ons interaksies in die digitale era kan geput kan word. Aanlyn sosiale-netwerke en mikroblog-dienste laat mense wêreldwyd toe om deel te neem in ’n openbare gesprek. Hierdie aanlyn gereedskap het egter inherente beperkinge op hoeveel kommunikasie prakties moontlik is. Wanneer gebruikers té gekonnekteer raak, word daar te veel ingligting ontvang om redelikerwys verwerk te kan word. Op Twitter (’n mikroblog-diens) is lyste ’n hulpmiddel waarmee gebruikers afsonderlike strome van inligting kan skep. Deur die gebruik van modelle van ‘volhoubare aanlyn diskoers’ en ‘inligtingoorlading’, bewys hierdie navorsing dat lyste ’n doeltreffende hulpmiddel is om die oorvloed van inligting te verlig op mikroblog-dienste. Kwantitatief volg gebruikers wat lyste gebruik meer gebruikers vergeleke met die wat nie lyste gebruik nie. Wanner hul lyste begin gebruik, volg hulle gebruikers teen ’n hoër koers as dié wat nie lyste gebruik nie. Kwalitatief bied die navorsing ook insig oor die redes vir die gebruik van lyste. Die navorsing onderstreep die akademiese relevansie van ‘inligtingoorlading’ en ‘aanlyn volhoubaarheid’ modelle wat nie voorheen gebruik is in die konteks van stroom-gebaseerde aanlyn gereedskap nie, en verdiep die begrip en belangrikheid van gebruiker-saamgestelde filtrering as ’n manier om die voordele te trek uit die toenemende oorvloed van inligting in die digitale era.
Wei, Chen. "Multi-collaborative filtering trust network for online recommendation systems." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2550571.
Full textReimer, James Allen. "On the recovery of images from partial information using [delta]²G filtering." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/29169.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Bergqvist, Martin, and Jim Glansk. "Fördelar med att applicera Collaborative Filtering på Steam : En utforskande studie." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14129.
Full textThe use of recommender systems is everywhere. On popular platforms such as Netflix and Amazon, you are always given new recommendations on what to consume next, based on your specific profiling. This is done by cross-referencing users and products to find probable patterns. The aims of this study were to compare the two main ways of generating recommendations, in an unorthodox dataset where “best practice” might not apply. Subsequently, recommendation efficiency was compared between Content Based Filtering and Collaborative Filtering, on the gaming-platform of Steam, in order to establish if there was potential for a better solution. We approached this by gathering data from Steam, building a representational baseline Content-based Filtering recommendation-engine based on what is currently used by Steam, and a competing Collaborative Filtering engine based on a standard implementation. In the course of this study, we found that while Content-based Filtering performance initially grew linearly as the player base of a game increased, Collaborative Filtering’s performance grew exponentially from a small player base, to plateau at a performance-level exceeding the comparison. The practical consequence of these findings would be the justification to apply Collaborative Filtering even on smaller, more complex sets of data than is normally done; The justification being that Content-based Filtering is easier to implement and yields decent results. With our findings showing such a big discrepancy even at basic models, this attitude might well change. The usage of Collaborative Filtering has been used scarcely on the more multifaceted datasets, but our results show that the potential to exceed Content-based Filtering is rather easily obtainable on such sets as well. This potentially benefits all purchase/community-combined platforms, as the usage of the purchase is monitorable on-line, and allows for the adjustments of misrepresentational factors as they appear.
Chotikakamthorn, Nopporn. "A pre-filtering maximum likelihood approach to multiple source direction estimation." Thesis, Imperial College London, 1996. http://hdl.handle.net/10044/1/8634.
Full textFournier, Kevin L. "Revisiting organizations as information processors organizational structure as a predictor of noise filtering." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://handle.dtic.mil/100.2/ADA483732.
Full textThesis Advisor(s): Pfeiffer, Karl. "June 2008." Description based on title screen as viewed on August 25, 2008. Includes bibliographical references (p. 49-52). Also available in print.
Andersson, Morgan. "Personal news video recommendations based on implicit feedback : An evaluation of different recommender systems with sparse data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234137.
Full textMängden video som finns tillgänglig på internet förväntas att tredubblas år 2021 jämfört med 2016. Detta innebär ett behov av sofistikerade filter för att kunna hantera detta informationsflöde. Detta examensarbete ämnar att svara på till vilken grad det går att generera personliga rekommendationer baserat på det data som nyhetsvideo innebär. Syftet är att utvärdera och jämföra olika rekommendationssystem och hur de står sig i ett användartest. Studien utfördes under våren 2018 och utvärderar fyra olika algoritmer. Dessa olika rekommendationssystem innefattar tekniker som content-based, collaborative-filter, hybrid och en popularitetsmodell används som basvärde. Det dataset som används är glest och har endast implicita attribut. Tre experiment utförs samt ett användartest. Mätpunkten för algoritmernas prestanda utgjordes av recall at 5 och recall at 10, dvs. att man mäter hur väl algoritmerna lyckas generera värdefulla rekommendationer i en topp-fem respektive topp-10-lista av videoklipp. Detta då det är av intresse att ha de mest relevanta videorna högst upp i sin lista av resultat. En jämförelse gjordes mellan olika mängd metadata som inkluderades vid träning. Ett annat test gick ut på att utforska hur algoritmerna presterar då datasetet blir mindre glest. I användartestet användes en utvärderingsmetod kallad mean-opinion-score och denna räknades ut per algoritm genom att testanvändare gav betyg på respektive rekommendation, baserat på hur intressant videon var för dem. Användartestet inkluderade även slumpmässigt generade videos för att kunna jämföras i form av basvärde. Resultaten indikerar, för detta dataset, att algoritmen content-based presterar bäst både med hänsyn till recall at 5 & 10 samt den totala poängen i användartestet. Alla algoritmer presterade bättre än slumpen.
Cenek, Martin. "Information Processing in Two-Dimensional Cellular Automata." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/275.
Full textHo, Yi Fong. "GA-based collaborative filtering for online recommendation." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1684526.
Full textBacklund, Alexander. "Switching hybrid recommender system to aid the knowledge seekers." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414623.
Full textOlsson, Jakob, and Viktor Yberg. "Log data filtering in embedded sensor devices." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175367.
Full textDatafiltrering är att ta bort onödig data i en datamängd, för att spara resurser såsom serverkapacitet och bandbredd. Metoden används för att minska mängden lagrad data och därmed förhindra att värdefulla resurser används för att bearbeta obetydlig information. Syftet med denna tes är att hitta algoritmer för datafiltrering och att undersöka vilken algoritm som ger bäst resultat i inbyggda system med resursbegränsningar. Det innebär att algoritmen bör vara resurseffektiv vad gäller minnesanvändning och prestanda, men spara tillräckligt många datapunkter för att inte modifiera eller förlora information. Efter att en algoritm har hittats kommer den även att implementeras för att passa Exqbe-systemet. Studien är genomförd genom att studera tidigare gjorda studier om datafiltreringsalgoritmer och dess applikationer. Jämförelser mellan flera välkända algoritmer har utförts för att hitta vilken som passar denna tes bäst. Jämförelsen mellan de olika filtreringsalgoritmerna resulterade i en implementation av en utökad version av Ramer-Douglas-Peucker-algoritmen. Algoritmen har optimerats och ett nytt filter har implementerats utöver algoritmen.
Ercan, Eda. "Probabilistic Matrix Factorization Based Collaborative Filtering With Implicit Trust Derived From Review Ratings Information." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612529/index.pdf.
Full textKuropka, Dominik. "Modelle zur Repräsentation natürlichsprachlicher Dokumente : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken /." Berlin : Logos-Verl, 2004. http://www.gbv.de/dms/ilmenau/toc/385143648kurop.PDF.
Full text