Dissertations / Theses on the topic 'Retrieval models'
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Stathopoulos, Vassilios. "Generative probabilistic models for image retrieval." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3360/.
Full textMorgan, Richard. "Component library retrieval using property models." Thesis, Durham University, 1991. http://etheses.dur.ac.uk/6095/.
Full textVasconcelos, Nuno Miguel Borges de Pinho Cruz de. "Bayesian models for visual information retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62947.
Full textIncludes bibliographical references (leaves 192-208).
This thesis presents a unified solution to visual recognition and learning in the context of visual information retrieval. Realizing that the design of an effective recognition architecture requires careful consideration of the interplay between feature selection, feature representation, and similarity function, we start by searching for a performance criteria that can simultaneously guide the design of all three components. A natural solution is to formulate visual recognition as a decision theoretical problem, where the goal is to minimize the probability of retrieval error. This leads to a Bayesian architecture that is shown to generalize a significant number of previous recognition approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, objective guidelines for controlling the trade-off between feature transformation and feature representation, and unified support for local and global queries without requiring image segmentation. The new architecture is shown to perform well on color, texture, and generic image databases, providing a good trade-off between retrieval accuracy, invariance, perceptual relevance of similarity judgments, and complexity. Because all that is needed to perform optimal Bayesian decisions is the ability to evaluate beliefs on the different hypothesis under consideration, a Bayesian architecture is not restricted to visual recognition. On the contrary, it establishes a universal recognition language (the language of probabilities) that provides a computational basis for the integration of information from multiple content sources and modalities. In result, it becomes possible to build retrieval systems that can simultaneously account for text, audio, video, or any other content modalities. Since the ability to learn follows from the ability to integrate information over time, this language is also conducive to the design of learning algorithms. We show that learning is, indeed, an important asset for visual information retrieval by designing both short and long-term learning mechanisms. Over short time scales (within a retrieval session), learning is shown to assure faster convergence to the desired target images. Over long time scales (between retrieval sessions), it allows the retrieval system to tailor itself to the preferences of particular users. In both cases, all the necessary computations are carried out through Bayesian belief propagation algorithms that, although optimal in a decision-theoretic sense, are extremely simple, intuitive, and easy to implement.
by Nuno Miguel Borges de Pinho Cruz de Vasconcelos.
Ph.D.
Mensink, Thomas. "Learning Image Classification and Retrieval Models." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM113/document.
Full textWe are currently experiencing an exceptional growth of visual data, for example, millions of photos are shared daily on social-networks. Image understanding methods aim to facilitate access to this visual data in a semantically meaningful manner. In this dissertation, we define several detailed goals which are of interest for the image understanding tasks of image classification and retrieval, which we address in three main chapters. First, we aim to exploit the multi-modal nature of many databases, wherein documents consists of images with a form of textual description. In order to do so we define similarities between the visual content of one document and the textual description of another document. These similarities are computed in two steps, first we find the visually similar neighbors in the multi-modal database, and then use the textual descriptions of these neighbors to define a similarity to the textual description of any document. Second, we introduce a series of structured image classification models, which explicitly encode pairwise label interactions. These models are more expressive than independent label predictors, and lead to more accurate predictions. Especially in an interactive prediction scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting trade-off between accuracy and manual labeling effort. We explore structured models for multi-label image classification, for attribute-based image classification, and for optimizing for specific ranking measures. Finally, we explore k-nearest neighbors and nearest-class mean classifiers for large-scale image classification. We propose efficient metric learning methods to improve classification performance, and use these methods to learn on a data set of more than one million training images from one thousand classes. Since both classification methods allow for the incorporation of classes not seen during training at near-zero cost, we study their generalization performances. We show that the nearest-class mean classification method can generalize from one thousand to ten thousand classes at negligible cost, and still perform competitively with the state-of-the-art
Rebai, Ahmed. "Interactive Object Retrieval using Interpretable Visual Models." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00608467.
Full textPérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.
Full textTam, Kwok Leung. "Indexing and retrieval of 3D articulated geometry models." Thesis, Durham University, 2009. http://etheses.dur.ac.uk/21/.
Full textMjali, Siyabonga Zimozoxolo. "Latent semantic models : a study of probabilistic models for text in information retrieval." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73881.
Full textMini Dissertation (MSc)--University of Pretoria, 2020.
The Hub Internship
Centre for Artificial Intelligence Research
Statistics
MSc (Mathematical Statistics)
Unrestricted
Belkacem, Thiziri. "Neural models for information retrieval : towards asymmetry sensitive approaches based on attention models." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30167.
Full textThis work is situated in the context of information retrieval (IR) using machine learning (ML) and deep learning (DL) techniques. It concerns different tasks requiring text matching, such as ad-hoc research, question answering and paraphrase identification. The objective of this thesis is to propose new approaches, using DL methods, to construct semantic-based models for text matching, and to overcome the problems of vocabulary mismatch related to the classical bag of word (BoW) representations used in traditional IR models. Indeed, traditional text matching methods are based on the BoW representation, which considers a given text as a set of independent words. The process of matching two sequences of text is based on the exact matching between words. The main limitation of this approach is related to the vocabulary mismatch. This problem occurs when the text sequences to be matched do not use the same vocabulary, even if their subjects are related. For example, the query may contain several words that are not necessarily used in the documents of the collection, including relevant documents. BoW representations ignore several aspects about a text sequence, such as the structure the context of words. These characteristics are important and make it possible to differentiate between two texts that use the same words but expressing different information. Another problem in text matching is related to the length of documents. The relevant parts can be distributed in different ways in the documents of a collection. This is especially true in large documents that tend to cover a large number of topics and include variable vocabulary. A long document could thus contain several relevant passages that a matching model must capture. Unlike long documents, short documents are likely to be relevant to a specific subject and tend to contain a more restricted vocabulary. Assessing their relevance is in principle simpler than assessing the one of longer documents. In this thesis, we have proposed different contributions, each addressing one of the above-mentioned issues. First, in order to solve the problem of vocabulary mismatch, we used distributed representations of words (word embedding) to allow a semantic matching between the different words. These representations have been used in IR applications where document/query similarity is computed by comparing all the term vectors of the query with all the term vectors of the document, regardless. Unlike the models proposed in the state-of-the-art, we studied the impact of query terms regarding their presence/absence in a document. We have adopted different document/query matching strategies. The intuition is that the absence of the query terms in the relevant documents is in itself a useful aspect to be taken into account in the matching process. Indeed, these terms do not appear in documents of the collection for two possible reasons: either their synonyms have been used or they are not part of the context of the considered documents. The methods we have proposed make it possible, on the one hand, to perform an inaccurate matching between the document and the query, and on the other hand, to evaluate the impact of the different terms of a query in the matching process. Although the use of word embedding allows semantic-based matching between different text sequences, these representations combined with classical matching models still consider the text as a list of independent elements (bag of vectors instead of bag of words). However, the structure of the text as well as the order of the words is important. Any change in the structure of the text and/or the order of words alters the information expressed. In order to solve this problem, neural models were used in text matching
Wessel, Raoul [Verfasser]. "Shape Retrieval Methods for Architectural 3D Models / Raoul Wessel." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1048091503/34.
Full textHörster, Eva. "Topic models for image retrieval on large-scale databases." kostenfrei kostenfrei, 2009. http://d-nb.info/998079553/34.
Full textLevy, Mark. "Retrieval and annotation of music using latent semantic models." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/2969.
Full textOsodo, Jennifer Akinyi. "An extended vector-based information retrieval system to retrieve e-learning content based on learner models." Thesis, University of Sunderland, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542053.
Full textRao, Ashwani Pratap. "Statistical information retrieval models| Experiments, evaluation on real time data." Thesis, University of Delaware, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1567821.
Full textWe are all aware of the rise of information age: heterogeneous sources of information and the ability to publish rapidly and indiscriminately are responsible for information chaos. In this work, we are interested in a system which can separate the "wheat" of vital information from the chaff within this information chaos. An efficient filtering system can accelerate meaningful utilization of knowledge. Consider Wikipedia, an example of community-driven knowledge synthesis. Facts about topics on Wikipedia are continuously being updated by users interested in a particular topic. Consider an automatic system (or an invisible robot) to which a topic such as "President of the United States" can be fed. This system will work ceaselessly, filtering new information created on the web in order to provide the small set of documents about the "President of the United States" that are vital to keeping the Wikipedia page relevant and up-to-date. In this work, we present an automatic information filtering system for this task. While building such a system, we have encountered issues related to scalability, retrieval algorithms, and system evaluation; we describe our efforts to understand and overcome these issues.
Amati, Giambattista. "Probability models for information retrieval based on divergence from randomness." Thesis, University of Glasgow, 2003. http://theses.gla.ac.uk/1570/.
Full textZhou, Xiaohua Hu Xiaohua. "Semantics-based language models for information retrieval and text mining /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2931.
Full textWang, Jun. "Probabilistic retrieval models : relationships, context-specific application, selection and implementation." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/655.
Full textTeevan, Jaime B. (Jaime Brooks) 1976. "Improving information retrieval with textual analysis : Bayesian models and beyond." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86759.
Full textConser, Erik Timothy. "Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3879.
Full textZarrinkoub, Sahand. "Transfer Learning in Deep Structured Semantic Models for Information Retrieval." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286310.
Full textNya modeller inom informationssökning inkluderar neurala nät som genererar vektorrepresentationer för sökfrågor och dokument. Dessa vektorrepresentationer används tillsammans med ett likhetsmått för att avgöra relevansen för ett givet dokument med avseende på en sökfråga. Semantiska särdrag i sökfrågor och dokument kan kodas in i vektorrepresentationerna. Detta möjliggör informationssökning baserat på semantiska enheter, vilket ej är möjligt genom de klassiska metoderna inom informationssökning, som istället förlitar sig på den ömsesidiga förekomsten av nyckelord i sökfrågor och dokument. För att träna neurala sökmodeller krävs stora datamängder. De flesta av dagens söktjänster används i för liten utsträckning för att möjliggöra framställande av datamängder som är stora nog att träna en neural sökmodell. Därför är det önskvärt att hitta metoder som möjliggör användadet av neurala sökmodeller i domäner med små tillgängliga datamängder. I detta examensarbete har en neural sökmodell implementerats och använts i en metod avsedd att förbättra dess prestanda på en måldatamängd genom att förträna den på externa datamängder. Måldatamängden som används är WikiQA, och de externa datamängderna är Quoras Question Pairs, Reuters RCV1 samt SquAD. I experimenten erhålls de bästa enskilda modellerna genom att föträna på Question Pairs och finjustera på WikiQA. Den genomsnittliga prestandan över ett flertal tränade modeller påverkas negativt av vår metod. Detta äller både när samtliga externa datamänder används tillsammans, samt när de används enskilt, med varierande prestanda beroende på vilken datamängd som används. Att förträna på RCV1 och Question Pairs ger den största respektive minsta negativa påverkan på den genomsnittliga prestandan. Prestandan hos en slumpmässigt genererad, otränad modell är förvånansvärt hög, i genomsnitt högre än samtliga förtränade modeller, och i nivå med BM25. Den bästa genomsnittliga prestandan erhålls genom att träna på måldatamängden WikiQA utan tidigare förträning.
Xu, Zhe. "Expertise Retrieval in Enterprise Microblogs with Enhanced Models and Brokers." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1399000265.
Full textDeng, Jie. "Emotion-based music retrieval and recommendation." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/82.
Full textDraeger, Marco. "Use of probabilistic topic models for search." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Sep/09Sep_Draeger.pdf.
Full textThesis Advisor(s): Squire, Kevin M. "September 2009." Description based on title screen as viewed on November 5, 2009. Author(s) subject terms: Document modeling, information retrieval, semantic search, Bayesian nonparametric methods, hierarchical Bayes. Includes bibliographical references (p. 67-71). Also available in print.
Park, Byung Chun. "Analytical models and optimal strategies for automated storage/retrieval system operations." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24568.
Full textBeecks, Christian [Verfasser]. "Distance-based similarity models for content-based multimedia retrieval / Christian Beecks." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2013. http://d-nb.info/1046647245/34.
Full textJonsson, Therese. "Data Retrieval Strategy for Modern Database Models in a Serverless Architecture." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279950.
Full textDen stigande närvaron av sociala medier formar de moderna systemarkitekturerna för att hantera prestanda och skala. I takt med att användarvolymer ökar växer också behovet av att hålla data konsistent och tillgängligt i datacenter över hela världen, vilket adderar komplexitet i distribuerade system. Startupen Leader Island har utvecklat en kommunikationsplattform för företag och organisationer, nyttjandes Amazon Web Services (AWS) som molnleverantör för att reducera operativa utmaningar. På plattformen delar användarna innehåll och interagerar med varandra. Att hämta data är en viktig komponent i platt- formen, eftersom användarna laddar in olika flöden och ska kunna söka efter specifikt innehåll. För denna funktionalitet använder Leader Island en kom- bination av AWS Elasticsearch Service för datainsamling och Amazon DynamoDB för permanent lagring. Denna konstellation har inneburit utmaningar inom data-modeller, data-mappning och hämtningsstrategier. Det här projektet syftar till att hitta bästa praxis för data-modeller och data-mappningar i båda instanser, samt undersöka nya strategier gällande datainsamling för att optimera prestandan. För detta konfigurerades tre designer med motsatta data- modeller och data-mappningar. Resultaten av varje design samlades in och mättes mot varandra. I huvudsak visade en design fördel, där datainsamlingen distribuerades över både Elasticsearch och DynamoDB. DynamoDB modellerades där enligt bästa praxis, och en reducerad datavolym propagerades till Elasticsearch, vilket resulterade i en 1.9 gånger bättre prestanda än i den initiala designen.
Doan, Khoa Dang. "Generative models meet similarity search: efficient, heuristic-free and robust retrieval." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/105052.
Full textDoctor of Philosophy
Searching for similar items, or similarity search, is one of the fundamental tasks in this information age, especially when there is a rapid growth of visual and textual contents. For example, in a search engine such as Google, a user searches for images with similar content to a referenced image; in online advertising, an advertiser finds new users, and eventually targets these users with advertisements, where the new users have similar profiles to some referenced users who have previously responded positively to the same or similar advertisements; in the chemical domain, scientists search for proteins with a similar structure to a referenced protein. The practical search applications in these domains often face several challenges, especially when these datasets or databases can contain a large number (e.g., millions or even billions) of complex-structured items (e.g., texts, images, and graphs). These challenges can be organized into three central themes: search efficiency (the economical use of resources such as computation and time) and model-design effort (the ease of building the search model). Besides search efficiency and model-design effort, it is increasingly a requirement of a search model to possess the ability to explain the search results, especially in the scientific domains where the items are structured objects such as graphs. This dissertation tackles the aforementioned challenges in practical search applications by using the computational techniques that learn to generate data. First, we overcome the need to scan the entire large dataset for similar items by considering an approximate similarity search technique called hashing. Then, we propose an unsupervised hashing framework that learns the hash functions with simpler objective functions directly from raw data. The proposed retrieval framework can be easily adapted into new domains with significantly lower effort in model design. When labeled data is available but is limited (which is a common scenario in practical search applications), we propose a hashing network that can synthesize additional data to improve the hash function learning process. The learned model also exhibits significant robustness against data corruption and slight changes in the underlying data. Finally, in domains with structured data such as graphs, we propose a computation approach that can simultaneously estimate the similarity of structured objects, such as graphs, and capture the alignment between their substructures, e.g., nodes. The alignment mechanism can help explain the reason why two objects are similar or dissimilar. This is a useful tool for domain experts who not only want to search for similar items but also want to understand how the search model makes its predictions.
Abdulahhad, Karam. "Information retrieval modeling by logic and lattice : application to conceptual information retrieval." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM014/document.
Full textThis thesis is situated in the context of logic-based Information Retrieval (IR) models. The work presented in this thesis is mainly motivated by the inadequate term-independence assumption, which is well-accepted in IR although terms are normally related, and also by the inferential nature of the relevance judgment process. Since formal logics are well-adapted for knowledge representation, and then for representing relations between terms, and since formal logics are also powerful systems for inference, logic-based IR thus forms a candidate piste of work for building effective IR systems. However, a study of current logic-based IR models shows that these models generally have some shortcomings. First, logic-based IR models normally propose complex, and hard to obtain, representations for documents and queries. Second, the retrieval decision d->q, which represents the matching between a document d and a query q, could be difficult to verify or check. Finally, the uncertainty measure U(d->q) is either ad-hoc or hard to implement. In this thesis, we propose a new logic-based IR model to overcome most of the previous limits. We use Propositional Logic (PL) as an underlying logical framework. We represent documents and queries as logical sentences written in Disjunctive Normal Form. We also argue that the retrieval decision d->q could be replaced by the validity of material implication. We then exploit the potential relation between PL and lattice theory to check if d->q is valid or not. We first propose an intermediate representation of logical sentences, where they become nodes in a lattice having a partial order relation that is equivalent to the validity of material implication. Accordingly, we transform the checking of the validity of d->q, which is a computationally intensive task, to a series of simple set-inclusion checking. In order to measure the uncertainty of the retrieval decision U(d->q), we use the degree of inclusion function Z that is capable of quantifying partial order relations defined on lattices. Finally, our model is capable of working efficiently on any logical sentence without any restrictions, and is applicable to large-scale data. Our model also has some theoretical conclusions, including, formalizing and showing the adequacy of van Rijsbergen assumption about estimating the logical uncertainty U(d->q) through the conditional probability P(q|d), redefining the two notions Exhaustivity and Specificity, and the possibility of reproducing most classical IR models as instances of our model. We build three operational instances of our model. An instance to study the importance of Exhaustivity and Specificity, and two others to show the inadequacy of the term-independence assumption. Our experimental results show worthy gain in performance when integrating Exhaustivity and Specificity into one concrete IR model. However, the results of using semantic relations between terms were not sufficient to draw clear conclusions. On the contrary, experiments on exploiting structural relations between terms were promising. The work presented in this thesis can be developed either by doing more experiments, especially about using relations, or by more in-depth theoretical study, especially about the properties of the Z function
Lupinetti, Katia. "Identification of shape and structural characteristics in assembly models for retrieval applications." Thesis, Paris, ENSAM, 2018. http://www.theses.fr/2018ENAM0003/document.
Full textThe large use of CAD systems in many industrial fields, such as automotive, naval, and aerospace, has generated a number of 3D databases making available a lot of 3D digital models. Within enterprises, which make use of these technologies, it is common practice to access to CAD models of previously developed products. In fact, designing new products often refers to existing models since similar products allow knowing in advance common problems and related solutions. Therefore, it is useful to have technological solutions that are able to evaluate the similarities of different products in such a way that the user can retrieve existing models and thus have access to the associated useful information for the new design.The concept of similarity has been widely studied in literature and it is well known that two objects can be similar under different perspectives. These multiple possibilities make complicate the assessment of the similarity between two objects. So far, many methods are proposed for the recognition of different parts similarities, but few researches address this problem for assembly models. If evaluating the similarity between two parts may be done under different perspectives, considering assemblies, the viewpoints increase considerably since there are more elements playing a meaningful role.Based on these requirements, we propose a system for retrieving similar assemblies according to different similarity criteria. To achieve this goal, it is necessary having an assembly description including all the information required for the characterizations of the possible different similarity criteria between the two assemblies. Therefore, one of the main topics of this work is the definition of a descriptor capable of encoding the data needed for the evaluation of similarity adaptable to different objectives. In addition, some of the information included in the descriptor may be available in CAD models, while other has to be extracted appropriately. Therefore, algorithms are proposed for extracting the necessary information to fill out the descriptor elements. Finally, for the evaluation of assembly similarity, several measures are defined, each of them evaluating a specific aspect of their similarity
Petkova, Desislava I. "Cluster-based relevance models for automatic image annotation /." Connect to online version, 2005. http://ada.mtholyoke.edu/setr/websrc/pdfs/www/2005/124.pdf.
Full textFei, Qi. "Operation models for information systems /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?IELM%202009%20FEI.
Full textUdoyen, Nsikan. "Information Modeling for Intent-based Retrieval of Parametric Finite Element Analysis Models." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14084.
Full textRenners, Ingo. "Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974452351.
Full textLester, Neil. "Assisting the software reuse process through classification and retrieval of software models." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311531.
Full textMarcén, Terraza Ana Cristina. "Design of a Machine Learning-based Approach for Fragment Retrieval on Models." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/158617.
Full text[CAT] L'aprenentatge automàtic (ML per les seues sigles en anglés) és conegut com la branca de la intel·ligència artificial que reuneix algorismes estadístics, probabilístics i d'optimització, que aprenen empíricament. ML pot aprofitar el coneixement i l'experiència que s'han generat durant anys en les empreses per a realitzar automàticament diferents processos. Per tant, ML s'ha aplicat a diverses àrees d'investigació, que estudien des de la medicina fins a l'enginyeria del programari. De fet, en el camp de l'enginyeria del programari, el manteniment i l'evolució d'un sistema abasta fins a un 80% de la vida útil del sistema. Les empreses, que s'han dedicat al desenvolupament de sistemes programari durant molts anys, han acumulat grans quantitats de coneixement i experiència. Per tant, ML resulta una solució atractiva per a reduir els seus costos de manteniment aprofitant els recursos acumulats. Específicament, la Recuperació d'Enllaços de Traçabilitat, la Localització d'Errors i la Ubicació de Característiques es troben entre les tasques més comunes i rellevants per a realitzar el manteniment de productes programari. Per a abordar aquestes tasques, els investigadors han proposat diferents enfocaments. No obstant això, la majoria de les investigacions se centren en mètodes tradicionals, com la indexació semàntica latent, que no explota els recursos recopilats. A més, la majoria de les investigacions s'enfoquen en el codi, descurant altres artefactes de programari com són els models. En aquesta tesi, presentem un enfocament basat en ML per a la recuperació de fragments en models (FRAME). L'objectiu d'aquest enfocament és recuperar el fragment del model que realitza millor una consulta específica. Això permet als enginyers recuperar el fragment que necessita ser traçat, reparat o situat per al manteniment del programari. Específicament, FRAME combina la computació evolutiva i les tècniques ML. En FRAME, un algorisme evolutiu és guiat per ML per a extraure de manera eficaç diferents fragments d'un model. Aquests fragments són posteriorment avaluats mitjançant tècniques ML. Per a aprendre a avaluar-los, les tècniques ML aprofiten el coneixement (fragments recuperats de models) i l'experiència que les empreses han generat durant anys. Basant-se en l'aprés, les tècniques ML determinen quin fragment del model realitza millor una consulta. No obstant això, la majoria de les tècniques ML no poden entendre els fragments dels models. Per tant, abans d'aplicar les tècniques ML, l'enfocament proposat codifica els fragments a través d'una codificació ontològica i evolutiva. En resum, FRAME està dissenyat per a extraure fragments d'un model, codificar-los i avaluar quin realitza millor una consulta específica. L'enfocament ha sigut avaluat a partir d'un cas real proporcionat pel nostre soci industrial (CAF, un proveïdor internacional de solucions ferroviàries). A més, els seus resultats han sigut comparats amb els resultats dels enfocaments més comuns i recents. Els resultats mostren que FRAME va obtindre els millors resultats per a la majoria dels indicadors de rendiment, proporcionant un valor mitjà de precisió igual a 59.91%, un valor mitjà d'exhaustivitat igual a 78.95%, una valor-F mig igual a 62.50% i un MCC (Coeficient de Correlació Matthews) mig igual a 0.64. Aprofitant els fragments recuperats dels models, FRAME és menys sensible al coneixement tàcit i al desajustament de vocabulari que els enfocaments basats en informació semàntica. No obstant això, FRAME està limitat per la disponibilitat de fragments recuperats per a dur a terme l'aprenentatge automàtic. Aquesta tesi presenta una discussió més àmplia d'aquests aspectes així com l'anàlisi estadística dels resultats, que avalua la magnitud de la millora en comparació amb els altres enfocaments.
[EN] Machine Learning (ML) is known as the branch of artificial intelligence that gathers statistical, probabilistic, and optimization algorithms, which learn empirically. ML can exploit the knowledge and the experience that have been generated for years to automatically perform different processes. Therefore, ML has been applied to a wide range of research areas, from medicine to software engineering. In fact, in software engineering field, up to an 80% of a system's lifetime is spent on the maintenance and evolution of the system. The companies, that have been developing these software systems for a long time, have gathered a huge amount of knowledge and experience. Therefore, ML is an attractive solution to reduce their maintenance costs exploiting the gathered resources. Specifically, Traceability Link Recovery, Bug Localization, and Feature Location are amongst the most common and relevant tasks when maintaining software products. To tackle these tasks, researchers have proposed a number of approaches. However, most research focus on traditional methods, such as Latent Semantic Indexing, which does not exploit the gathered resources. Moreover, most research targets code, neglecting other software artifacts such as models. In this dissertation, we present an ML-based approach for fragment retrieval on models (FRAME). The goal of this approach is to retrieve the model fragment which better realizes a specific query in a model. This allows engineers to retrieve the model fragment, which must be traced, fixed, or located for software maintenance. Specifically, the FRAME approach combines evolutionary computation and ML techniques. In the FRAME approach, an evolutionary algorithm is guided by ML to effectively extract model fragments from a model. These model fragments are then assessed through ML techniques. To learn how to assess them, ML techniques takes advantage of the companies' knowledge (retrieved model fragments) and experience. Then, based on what was learned, ML techniques determine which model fragment better realizes a query. However, model fragments are not understandable for most ML techniques. Therefore, the proposed approach encodes the model fragments through an ontological evolutionary encoding. In short, the FRAME approach is designed to extract model fragments, encode them, and assess which one better realizes a specific query. The approach has been evaluated in our industrial partner (CAF, an international provider of railway solutions) and compared to the most common and recent approaches. The results show that the FRAME approach achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC (Matthews correlation coefficient) value of 0.64. Leveraging retrieved model fragments, the FRAME approach is less sensitive to tacit knowledge and vocabulary mismatch than the approaches based on semantic information. However, the approach is limited by the availability of the retrieved model fragments to perform the learning. These aspects are further discussed, after the statistical analysis of the results, which assesses the magnitude of the improvement in comparison to the other approaches.
Marcén Terraza, AC. (2020). Design of a Machine Learning-based Approach for Fragment Retrieval on Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158617
TESIS
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Full textOnline photo sharing systems provide a valuable source of user generated content (UGC). Most Web image retrieval systems use textual annotations to rank the results, although these annotations do not only illustrate the visual content of an image, but also describe subjective, spatial, temporal, and social dimensions, complicating the task of keyword based search. The research in this thesis is focused on how to improve the retrieval of images in large scale context , i.e. the Web, using information provided by users combined with visual content from images. Di erent forms of UGC are explored, such as textual annotations, visual annotations, and click-through-data, as well as di erent techniques to combine these data to improve the retrieval of images using visual information. In conclusion, the research conducted in this thesis focuses on the impor- tance to include visual information into various steps of the retrieval of media content. Using visual information, in combination with various forms of UGC, can signi cantly improve the retrieval performance and alter the user experience when searching for multimedia content on the Web. 1
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