Dissertations / Theses on the topic 'Similarity-based Reasoning'
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Jurisica, Igor. "TA3, theory, implementation, and applications of similarity-based retrieval for case-based reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35199.pdf.
Full textGalushka, Mykola. "Discovering and managing similarity knowledge in temporal case-based reasoning systems." Thesis, University of Ulster, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535142.
Full textWagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.
Full textWagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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Steffens, Timo. "Enhancing similarity measures with imperfect rule-based background knowledge." Doctoral thesis, Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2898562&prov=M&dok_var=1&dok_ext=htm.
Full textEl-Mehalawi, Mohamed. "A geometric similarity case-based reasoning system for cost estimation in net-shape manufacturing /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488186329504367.
Full textWolf, Markus Adrian. "Applying case based reasoning and structural similarity for effective retrieval of expert knowledge from software designs." Thesis, University of Greenwich, 2012. http://gala.gre.ac.uk/11978/.
Full textNordström, Markus. "Automatic Source Code Classification : Classifying Source Code for a Case-Based Reasoning System." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25519.
Full textQvarford, Johannes. "EN SCHACK AI BASERAD PÅ CASE-BASED REASONING MED GRUNDLIG LIKHET." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11049.
Full textSaeed, Soran. "An investigation into establishing a generalised approach for defining similarity metrics between 3D shapes for the casting design problem in case-based reasoning (CBR)." Thesis, University of Greenwich, 2006. http://gala.gre.ac.uk/6288/.
Full textWalker, Donald. "Similarity Determination and Case Retrieval in an Intelligent Decision Support System for Diabetes Management." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194562654.
Full textYan, Wei. "(Meta)Knowledge modeling for inventive design." Thesis, Strasbourg, 2014. http://www.theses.fr/2014STRAD006/document.
Full textAn increasing number of industries feel the need to formalize their innovation processes. In this context, quality domain tools show their limits as well as the creativity assistance approaches derived from brainstorming. TRIZ (Theory of Inventive Problem Solving) appears to be a pertinent answer to these needs. Developed in the middle of the 20th century by G. S. Althshuller, this methodology's goal was initially to improve and facilitate the resolution of technological problems. According to TRIZ, the resolution of inventive problems consists of the construction of models and the use of the corresponding knowledge sources. Different models and knowledge sources were established in order to solve different types of inventive problems, such as the forty inventive principles for eliminating the technical contradictions. These knowledge sources with different levels of abstraction are all built independent of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the inventive problem solving process, the development of an "intelligent knowledge manager" is explored in this thesis. On the one hand, according to the TRIZ knowledge sources ontologies, the manager offers to the users the relevant knowledge sources associated to the model they are building. On the other hand, the manager has the ability to fill "automatically" the models of the other knowledge sources. These research works aim at facilitating and automating the process of solving inventive problems based on semantic similarity and ontology techniques. At first, the TRIZ knowledge sources are formalized based on ontologies, such that heuristic inference can be executed to search for specific solutions. Then, methods for calculating semantic similarity are explored to search and define the missing links among the TRIZ knowledge sources. In order to solve inventive problems, the TRIZ user firstly chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other specific solutions are returned through ontology inference. Finally, a software prototype is developed based on semantic similarity and ontology inference to support this automatic process of solving inventive problems
Griffiths, Anthony D. "Inductive generalisation in case-based reasoning systems." Thesis, University of York, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336844.
Full textEyorokon, Vahid. "Measuring Goal Similarity Using Concept, Context and Task Features." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091.
Full textMiller, Gina L. "An empirical investigation of a categorization based model of the evaluation formation process as it pertains to set membership prediction." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/29984.
Full textWalker, Donald. "Similarity determination and case retrieval in an intelligent decision support system for diabetes managment." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1194562654.
Full textKhelif, Racha. "Estimation du RUL par des approches basées sur l'expérience : de la donnée vers la connaissance." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2019/document.
Full textOur thesis work is concerned with the development of experience based approachesfor criticalcomponent prognostics and Remaining Useful Life (RUL) estimation. This choice allows us to avoidthe problematic issue of setting a failure threshold.Our work was based on Case Based Reasoning (CBR) to track the health status of a new componentand predict its RUL. An Instance Based Learning (IBL) approach was first developed offering twoexperience formalizations. The first is a supervised method that takes into account the status of thecomponent and produces health indicators. The second is an unsupervised method that fuses thesensory data into degradation trajectories.The approach was then evolved by integrating knowledge. Knowledge is extracted from the sensorydata and is of two types: temporal that completes the modeling of instances and frequential that,along with the similarity measure refine the retrieval phase. The latter is based on two similaritymeasures: a weighted one between fixed parallel windows and a weighted similarity with temporalprojection through sliding windows which allow actual health status identification.Another data-driven technique was tested. This one is developed from features extracted from theexperiences that can be either mono or multi-dimensional. These features are modeled by a SupportVector Regression (SVR) algorithm. The developed approaches were assessed on two types ofcritical components: turbofans and ”Li-ion” batteries. The obtained results are interesting but theydepend on the type of the treated data
Shah, Raza. "Property inference decision-making and decision switching of undergraduate engineers : implications for ideational diversity & fluency through movements in a Cartesian concept design space." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/278700.
Full textChang, Lu-Ping, and 張履平. "Parallelized similarity indexing technology for Case-based reasoning." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/29821167439256356445.
Full text國立交通大學
資訊科學系
88
Case-based reasoning (CBR) is a methodology of problem-solving in artificial intelligence. Just like human being, CBR uses prior cases to find out suitable solution for the new problems. Unlike the others, CBR pays attention to the characteristics of each case. CBR can correctly take advantage of the situations and methods in former cases to solve problems. A critical task of CBR is to retrieve similar prior cases accurately and many researchers have proposed some useful technologies to handle such problem. However, increasingly larger number of cases influences the performance of retrieving similar cases for the large-scale CBR was seldom been discussed. In this thesis, the performance issue of large-scale CBR is discussed and a new indexing method, called bit-wise indexing method, and the corresponding efficient algorithms are proposed for retrieving the similar cases in large-scale CBR efficiently. The bit-wise indexing method and the corresponding algorithm can be easily parallelized and thus gets great performance improvement in case retrieving and similarity measuring. Some experiments are made for comparing the performance with other methods and the results show the performance of proposed method is admirable.
CHEN, CHI WEN, and 陳啟文. "Spatial reasoning and similarity retrieval using vector-based representation." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/09691497420668101232.
Full textBaig, Mariam. "Case-based reasoning - An effective paradigm for providing diagnostic support for stroke patients." Thesis, 2008. http://hdl.handle.net/1974/1488.
Full textThesis (Master, Computing) -- Queen's University, 2008-09-27 11:14:04.85
Fernandes, Bruno Filipe Martins. "Handle default data with case-based reasoning: an approach to solve problem reports." Master's thesis, 2014. http://hdl.handle.net/1822/36563.
Full textOn a business context, it is responsibility of the Software Product Support Team analyze and solve, if necessary, problems that may arise on software products. Sometimes, the reported problems are not a real defect, i.e., sometimes the client does not have a full understanding about all features of the software product. The team must evaluate and analyze all the Problem Reports that arrive every day. As products are spread across different customers, it is normal to have Problem Reports that are very similar to others that have already been solved for other clients and/or by another member of the Support Team. This dissertation proposes the development of a system that is able to analyze a Problem Report and then provide past problems that are similar to the one being analyzed. An artificial intelligence technique, named Case-Based Reasoning, will be used to achieve such goals. Existent Case-Based Reasoning systems are neither complete nor adaptable to specific domains since the effort to adapt either the reasoning process or the knowledge representation mechanism, to a new domain, is too high. To address such drawbacks, a generic reasoning component will be designed and developed. This dissertation introduces a new approach to the typical Case-Based Reasoning cycle where is possible to handle default, unknown and incomplete data.
Num contexto empresarial, é da responsabilidade da equipa de Suporte ao Produto de Software analisar e corrigir, se necessário, problemas que possam surgir em produtos de software. Muitas vezes esses mesmos problemas não o são, isto é, muitas vezes é o cliente que não tem uma percepção completa do funcionamento do produto. A equipa deve avaliar e analisar todos os Problem Reports que vão chegando dia após dia. Como os produtos se encontram espalhados por diferentes clientes, é normal aparecerem problemas que são muito semelhantes com outros que já foram resolvidos para outros clientes e/ou por um outro membro da equipa de Suporte. Esta dissertação propõe o desenvolvimento de um sistema que seja capaz de analisar um problema e indicar problemas passados que sejam semelhantes com o que se está analisar. Uma técnica da inteligência artificial, de seu nome Raciocínio Baseado em Casos, será utilizada para atingir tais objectivos. Os sistemas já existentes que usam esta técnica não são completos nem adaptáveis a domínios específicos, uma vez que o esforço para adaptar tanto o processo de raciocínio como a representação do conhecimento, para um novo domínio, é demasiado elevado. Para solucionar tais problemas, um componente de raciocínio genérico será especificado e desenvolvido. Esta dissertação introduz uma nova abordagem ao ciclo do Raciocínio-Baseado em Casos onde é possível tratar informação desconhecida.
Janusz, Andrzej. "Algorithms for Similarity Relation Learning from High Dimensional Data." Doctoral thesis, 2014. https://depotuw.ceon.pl/handle/item/607.
Full textPojęcie podobieństwa pełni istotną rolę w dziedzinach uczenia maszynowego i sztucznej inteligencji. Jest ono powszechnie wykorzystywane w zadaniach dotyczących nadzorowanej klasyfikacji, grupowania, wykrywania nietypowych obiektów oraz planowania. Ponadto w dziedzinach takich jak wyszukiwanie informacji (ang. information retrieval) lub wnioskowanie na podstawie przykładów (ang. case-based reasoning) pojęcie podobieństwa jest kluczowe ze względu na jego obecność na wszystkich etapach wyciągania wniosków. Jednakże samo podobieństwo jest pojęciem niezwykle złożonym i wymyka się próbom ścisłego zdefiniowania. Stopień podobieństwa między dwoma obiektami może być różny w zależności od kontekstu w jakim się go rozpatruje. W praktyce trudno jest nawet ocenić jakość otrzymanych stopni podobieństwa bez odwołania się do zadania, któremu mają służyć. Z tego właśnie powodu modele oceniające podobieństwo powinny być wyuczane na podstawie danych, specjalnie na potrzeby realizacji konkretnego zadania. W niniejszej rozprawie opisano model podobieństwa zwany Regułowym Modelem Podobieństwa (ang. Rule-Based Similarity) oraz zaproponowano algorytm tworzenia tego modelu na podstawie danych. Wykorzystuje on elementy teorii zbiorów przybliżonych do konstruowania funkcji podobieństwa pozwalającej aproksymować podobieństwo w zadanym kontekście. Konstrukcja ta rozpoczyna się od wykrywania zbiorów wysokopoziomowych cech obiektów. Mogą być one interpretowane jako istotne aspekty podobieństwa. Mając zdefiniowane tego typu cechy możliwe jest wykorzystanie idei modelu kontrastu cech Tversky’ego (ang. feature contrast model) do budowy precyzyjnej oraz zgodnej z obserwacjami psychologów funkcji podobieństwa dla rozważanego problemu. Dodatkowo, niniejsza rozprawa zawiera opis dwóch rozszerzeń Regułowego Modelu Podobieństwa przystosowanych do działania na danych o bardzo wielu atrybutach. Starają się one włączyć do modelu szerszy zakres aspektów podobieństwa. W pierwszym z nich odbywa się to poprzez konstruowanie wielu zbiorów cech z reduktów decyzyjnych. Aby zapewnić ich zróżnicowanie, zaproponowano algorytm łączący heurystykę zachłanna z elementami losowymi. Podejście to jest szczególnie wskazane dla zadań związanych z problemem małej liczby obiektów i dużej liczby cech (ang. the few-objects-many-attributes problem), np. analizy danych mikromacierzowych. Podobny pomysł może być również wykorzystany w dziedzinie analizy tekstów. Realizowany jest on przez drugie z proponowanych rozszerzeń modelu. Łączy ono metodę semantycznego indeksowania z algorytmem obliczania bireduktów informacyjnych, aby reprezentować teksty dobrze zdefiniowanymi pojęciami. Funkcja podobieństwa zaproponowanego modelu może być wykorzystana do klasyfikacji nowych obiektów oraz do łączenia dokumentów tekstowych w semantycznie spójne grupy. Eksperymenty, których wyniki opisano w rozprawie, dowodzą, ze zaproponowane modele mogą skutecznie konkurować nawet z powszechnie uznanymi rozwiązaniami.
Hui-MeiLin and 林惠美. "Supplier Selection for the Components of New Product by Case-Based Reasoning and Technique for Order Preference by Similarity to Ideal Solution." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/95632073689047580154.
Full text國立成功大學
工業與資訊管理學系專班
101
Supplier selection is one of the most important jobs of supply chain management. Because of the reducing of product life cycle, manufacturers have to utilize the existing supply chain to shorten the time from design to market as well as improve their competition. In fact, buyers will nominate the potential suppliers by their expertise and experience to decide the most appropriate candidates based on the RFQ (request for quotation) and negotiation result. However, different buyers lead to different potential supplier list and supplier evaluation outcomes. Therefore, it becomes an important issue to find the most appropriate supplier regardless of the differences of the expertise and experience of buyers. Similar to the purchasing process, we propose a two-stage method for supplier selection. The first stage employs the case-based reasoning method to find potential suppliers by calculating the cosine similarity of new material description with the old ones stored in a database. Then the technique for order preference by similarity to ideal solution (TOPSIS) is used in the second stage to rank the potential suppliers by their performance that is evaluated by the following factors: quality, delivery, cost, and supply possibility. The weights of the factors are set up in advance by decision makers. For the case analyzed in this study, the most appropriate supplier determined by the TOPSIS method is identical to the one suggested by a senior professional buyer. This demonstrates that the method proposed by this study can be helpful to the buyers for supplier selection.
Chen, Chia-Ling, and 陳佳伶. "New Fuzzy Interpolative Reasoning Methods Based on Ranking Values of Polygonal Fuzzy Sets, Automatically Generated Weights of Fuzzy Rules and Similarity Measures Between Polygonal Fuzzy Sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47496074659379726144.
Full text國立臺灣科技大學
資訊工程系
103
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and the ranking values of polygonal fuzzy sets. In the first method of our thesis, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. The experimental results show that the proposed method can overcome the drawbacks of the existing fuzzy interpolative reasoning methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive fuzzy interpolation method based on ranking values of polygonal fuzzy sets and similarity measures between polygonal fuzzy sets. The proposed adaptive fuzzy interpolation method performs fuzzy interpolative reasoning using multiple fuzzy rules with multiple antecedent variables and solves the contradictions after the fuzzy interpolative reasoning processes based on similarity measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolation method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
Lakhlili, Zakia. "TAARAC : test d'anglais adaptatif par raisonnement à base de cas." Thèse, 2007. http://hdl.handle.net/1866/7206.
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