Дисертації з теми "Retrieval-based learning"
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Maleki-Dizaji, Saeedeh. "Evolutionary learning multi-agent based information retrieval systems." Thesis, Sheffield Hallam University, 2003. http://shura.shu.ac.uk/6856/.
Повний текст джерелаWu, Mengjiao. "Retrieval-based Metacognitive Monitoring in Self-regulated Learning." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532049448140424.
Повний текст джерелаChafik, Sanaa. "Machine learning techniques for content-based information retrieval." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL008/document.
Повний текст джерелаThe amount of media data is growing at high speed with the fast growth of Internet and media resources. Performing an efficient similarity (nearest neighbor) search in such a large collection of data is a very challenging problem that the scientific community has been attempting to tackle. One of the most promising solutions to this fundamental problem is Content-Based Media Retrieval (CBMR) systems. The latter are search systems that perform the retrieval task in large media databases based on the content of the data. CBMR systems consist essentially of three major units, a Data Representation unit for feature representation learning, a Multidimensional Indexing unit for structuring the resulting feature space, and a Nearest Neighbor Search unit to perform efficient search. Media data (i.e. image, text, audio, video, etc.) can be represented by meaningful numeric information (i.e. multidimensional vector), called Feature Description, describing the overall content of the input data. The task of the second unit is to structure the resulting feature descriptor space into an index structure, where the third unit, effective nearest neighbor search, is performed.In this work, we address the problem of nearest neighbor search by proposing three Content-Based Media Retrieval approaches. Our three approaches are unsupervised, and thus can adapt to both labeled and unlabeled real-world datasets. They are based on a hashing indexing scheme to perform effective high dimensional nearest neighbor search. Unlike most recent existing hashing approaches, which favor indexing in Hamming space, our proposed methods provide index structures adapted to a real-space mapping. Although Hamming-based hashing methods achieve good accuracy-speed tradeoff, their accuracy drops owing to information loss during the binarization process. By contrast, real-space hashing approaches provide a more accurate approximation in the mapped real-space as they avoid the hard binary approximations.Our proposed approaches can be classified into shallow and deep approaches. In the former category, we propose two shallow hashing-based approaches namely, "Symmetries of the Cube Locality Sensitive Hashing" (SC-LSH) and "Cluster-based Data Oriented Hashing" (CDOH), based respectively on randomized-hashing and shallow learning-to-hash schemes. The SC-LSH method provides a solution to the space storage problem faced by most randomized-based hashing approaches. It consists of a semi-random scheme reducing partially the randomness effect of randomized hashing approaches, and thus the memory storage problem, while maintaining their efficiency in structuring heterogeneous spaces. The CDOH approach proposes to eliminate the randomness effect by combining machine learning techniques with the hashing concept. The CDOH outperforms the randomized hashing approaches in terms of computation time, memory space and search accuracy.The third approach is a deep learning-based hashing scheme, named "Unsupervised Deep Neuron-per-Neuron Hashing" (UDN2H). The UDN2H approach proposes to index individually the output of each neuron of the top layer of a deep unsupervised model, namely a Deep Autoencoder, with the aim of capturing the high level individual structure of each neuron output.Our three approaches, SC-LSH, CDOH and UDN2H, were proposed sequentially as the thesis was progressing, with an increasing level of complexity in terms of the developed models, and in terms of the effectiveness and the performances obtained on large real-world datasets
Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.
Повний текст джерелаAlzu’bi, Ahmad Gazi Suleiman. "Semantic content-based image retrieval using compact multifeatures and deep learning." Thesis, University of the West of Scotland, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738480.
Повний текст джерелаcom, chungkp@yahoo, and Kien Ping Chung. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070831.123947.
Повний текст джерелаChung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Thesis, Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/666/.
Повний текст джерелаChung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. http://researchrepository.murdoch.edu.au/666/.
Повний текст джерелаWu, Zutao. "Kmer-based sequence representations for fast retrieval and comparison." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103083/1/Zutao_Wu_Thesis.pdf.
Повний текст джерелаShevchuk, Danylo. "Audio Moment Retrieval based on Natural Language Query." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20094.
Повний текст джерелаMarcén, 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.
Повний текст джерела[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
Osodo, 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.
Повний текст джерелаTang, Siu-shing. "Integrating distance function learning and support vector machine for content-based image retrieval /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?CSED%202006%20TANG.
Повний текст джерелаDey, Sounak. "Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671082.
Повний текст джерелаEl diluvio de contenido visual en Internet, desde contenido generado por el usuario hasta colecciones de imágenes comerciales, motiva nuevos métodos intuitivos para buscar contenido de imágenes digitales: ¿cómo podemos encontrar ciertas imágenes en una base de datos de millones? La recuperación de imágenes basada en bocetos (SBIR) es un tema de investigación emergente en el que se puede usar un dibujo a mano libre para consultar visualmente imágenes fotográficas. SBIR está alineado con las tendencias emergentes para el consumo de contenido visual en dispositivos móviles con pantalla táctil, para los cuales las interacciones gestuales como el boceto son una alternativa natural a la entrada de texto. Esta tesis presenta varias contribuciones a la literatura de SBIR. En primer lugar, proponemos un marco de aprendizaje multimodal que mapea tanto los bocetos como el texto en un espacio de incrustación conjunto invariante al estilo representativo, al tiempo que conserva la semántica. La incrustación resultante permite la comparación directa y la búsqueda entre bocetos / texto e imágenes y se basa en una red neuronal convolucional de múltiples ramas (CNN) entrenada utilizando esquemas de entrenamiento únicos. La incrustación profundamente aprendida muestra un rendimiento de recuperación de última generación en varios puntos de referencia SBIR. En segundo lugar, proponemos un enfoque para la recuperación de imágenes multimodales en imágenes con etiquetas múltiples. Una arquitectura de red profunda multimodal está formulada para modelar conjuntamente bocetos y texto como modalidades de consulta de entrada en un espacio de incrustación común, que luego se alinea aún más con el espacio de características de la imagen. Nuestra arquitectura también se basa en una detección de objetos sobresalientes a través de un modelo de atención visual supervisado basado en LSTM aprendido de las características convolucionales. Tanto la alineación entre las consultas y la imagen como la supervisión de la atención en las imágenes se obtienen generalizando el algoritmo húngaro utilizando diferentes funciones de pérdida. Esto permite codificar las características basadas en objetos y su alineación con la consulta independientemente de la disponibilidad de la concurrencia de diferentes objetos en el conjunto de entrenamiento. Validamos el rendimiento de nuestro enfoque en conjuntos de datos estándar de objeto único / múltiple, mostrando el rendimiento más avanzado en cada conjunto de datos SBIR. En tercer lugar, investigamos el problema de la recuperación de imágenes basadas en bocetos de disparo cero (ZS-SBIR), donde los bocetos humanos se utilizan como consultas para llevar a cabo la recuperación de fotos de categorías invisibles. Avanzamos de manera importante en las técnicas anteriores al proponer un nuevo escenario ZS-SBIR que representa un firme paso adelante en su aplicación práctica. El nuevo entorno reconoce de manera única dos desafíos importantes pero a menudo descuidados de la práctica ZS-SBIR, (i) la gran brecha de dominio entre el boceto aficionado y la foto, y (ii) la necesidad de avanzar hacia la recuperación a gran escala. Primero contribuimos a la comunidad con un nuevo conjunto de datos ZS-SBIR, QuickDraw -Extended, que consta de bocetos de $ 330,000 $ y fotos de $ 204,000 $ que abarcan 110 categorías. Los bocetos humanos aficionados altamente abstractos se obtienen a propósito para maximizar la brecha de dominio, en lugar de los incluidos en los conjuntos de datos existentes que a menudo pueden ser semi-fotorrealistas. Luego formulamos un marco ZS-SBIR para modelar conjuntamente bocetos y fotos en un espacio de incrustación común.
The deluge of visual content on the Internet – from user-generated content to commercial image collections - motivates intuitive new methods for searching digital image content: how can we find certain images in a database of millions? Sketch-based image retrieval (SBIR) is an emerging research topic in which a free-hand drawing can be used to visually query photographic images. SBIR is aligned to emerging trends for visual content consumption on mobile touch-screen based devices, for which gestural interactions such as sketch are a natural alternative to textual input. This thesis presents several contributions to the literature of SBIR. First, we propose a cross-modal learning framework that maps both sketches and text into a joint embedding space invariant to depictive style, while preserving semantics. The resulting embedding enables direct comparison and search between sketches/text and images and is based upon a multi-branch convolutional neural network (CNN) trained using unique training schemes. The deeply learned embedding is shown to yield state-of-art retrieval performance on several SBIR benchmarks. Second, we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sket-ches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model lear-ned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every SBIR dataset. Third, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of $330,000$ sketches and $204,000$ photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset.
Mansjur, Dwi Sianto. "Statistical pattern recognition approaches for retrieval-based machine translation systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42821.
Повний текст джерелаLinckels, Serge. "An e-librarian service : supporting explorative learning by a description logics based semantic retrieval tool." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2008/1745/.
Повний текст джерелаObwohl sich die Verfügbarkeit von pädagogischen Inhalten in elektronischer Form stetig erhöht, ist deren Nutzen in einem schulischen Umfeld recht gering. Die Hauptursache dessen ist, dass es zu viele unzuverlässige, redundante und nicht relevante Informationen gibt. Das Finden von passenden Lernobjekten ist eine schwierige Aufgabe, die vom benutzerbasierten Filtern der passenden Informationen abhängig ist. Damit Wissensbanken wie das online Tele-TASK Archiv zu nützlichen, pädagogischen Ressourcen werden, müssen Lernobjekte korrekt, zuverlässig und in maschinenverständlicher Form identifiziert werden, sowie effiziente Suchwerkzeuge entwickelt werden. Unser Ziel ist es, einen E-Bibliothekar-Dienst zu schaffen, der multimediale Ressourcen in einer Wissensbank auf effizientere Art und Weise findet als mittels Navigieren durch ein Inhaltsverzeichnis oder mithilfe einer einfachen Stichwortsuche. Unsere Prämisse ist, dass passendere Ergebnisse gefunden werden könnten, wenn die semantische Suchmaschine den Sinn der Benutzeranfrage verstehen würde. In diesem Fall wären die gelieferten Antworten logische Konsequenzen einer Inferenz und nicht die einer Schlüsselwortsuche. Tests haben gezeigt, dass unser E-Bibliothekar-Dienst unter allen Dokumenten in einer gegebenen Wissensbank diejenigen findet, die semantisch am besten zur Anfrage des Benutzers passen. Dabei gilt, dass der Benutzer eine vollständige und präzise Antwort erwartet, die keine oder nur wenige Zusatzinformationen enthält. Außerdem ist unser System in der Lage, dem Benutzer die Qualität und Pertinenz der gelieferten Antworten zu quantifizieren und zu veranschaulichen. Schlussendlich liefert unser E-Bibliothekar-Dienst dem Benutzer immer eine Antwort, selbst wenn das System feststellt, dass es keine vollständige Antwort auf die Frage gibt. Unser E-Bibliothekar-Dienst ermöglicht es dem Benutzer, seine Fragen in einer sehr einfachen und menschlichen Art und Weise auszudrücken, nämlich in natürlicher Sprache. Linguistische Informationen und ein gegebener Kontext in Form einer Ontologie werden für die semantische Übersetzung der Benutzereingabe in eine logische Form benutzt. Unser E-Bibliothekar-Dienst wurde prototypisch in drei unterschiedliche pädagogische Werkzeuge umgesetzt. In zwei Experimenten wurde in einem pädagogischen Umfeld die Angemessenheit und die Zuverlässigkeit dieser Werkzeuge als Komplement zum klassischen Unterricht geprüft. Die Hauptergebnisse sind folgende: Erstens wurde festgestellt, dass Schüler generell akzeptieren, ganze Fragen einzugeben - anstelle von Stichwörtern - wenn dies ihnen hilft, bessere Suchresultate zu erhalten. Zweitens, das wichtigste Resultat aus den Experimenten ist die Erkenntnis, dass Schuleresultate verbessert werden können, wenn Schüler unseren E-Bibliothekar-Dienst verwenden. Wir haben eine generelle Verbesserung von 5% der Schulresultate gemessen. 50% der Schüler haben ihre Schulnoten verbessert, 41% von ihnen sogar maßgeblich. Einer der Hauptgründe für diese positiven Resultate ist, dass die Schüler motivierter waren und folglich bereit waren, mehr Einsatz und Fleiß in das Lernen und in das Erwerben von neuem Wissen zu investieren.
Azuaje, Francisco Javier. "An unsupervised neural learning approach to retrieval strategies for case-based reasoning and decision support." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311877.
Повний текст джерелаEgli, Sebastian [Verfasser], and Jörg [Akademischer Betreuer] Bendix. "Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning / Sebastian Egli ; Betreuer: Jörg Bendix." Marburg : Philipps-Universität Marburg, 2019. http://d-nb.info/1187443476/34.
Повний текст джерелаTurner, Sarah J. "Why Do College Students Improve their Learning Performance Across Trials?" Kent State University Honors College / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ksuhonors1334854997.
Повний текст джерелаRossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаChang, Ran. "Effective Graph-Based Content--Based Image Retrieval Systems for Large-Scale and Small-Scale Image Databases." DigitalCommons@USU, 2013. https://digitalcommons.usu.edu/etd/2123.
Повний текст джерелаBelkacem, Thiziri. "Neural models for information retrieval : towards asymmetry sensitive approaches based on attention models." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30167.
Повний текст джерелаThis 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
Wilhelm-Stein, Thomas. "Information Retrieval in der Lehre." Doctoral thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-199778.
Повний текст джерелаInformation retrieval has achieved great significance in form of search engines for the Internet. Retrieval systems are used in a variety of research scenarios, including corporate support databases, but also for the organization of personal emails. A current challenge is to determine and predict the performance of individual components of these retrieval systems, in particular the complex interactions between them. For the implementation and configuration of retrieval systems and retrieval components professionals are needed. By using the web-based learning application Xtrieval Web Lab students can gain practical knowledge about the information retrieval process by arranging retrieval components in a retrieval system and their evaluation without using a programming language. Game mechanics guide the students in their discovery process, motivate them and prevent information overload by a partition of the learning content
Yuee, Liu. "Ontology-based image annotation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39611/1/Liu_Yuee_Thesis.pdf.
Повний текст джерелаMoreux, Jean-Philippe, and Guillaume Chiron. "Image Retrieval in Digital Libraries: A Large Scale Multicollection Experimentation of Machine Learning techniques." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16444.
Повний текст джерелаSi historiquement, les bibliothèques numériques patrimoniales furent d’abord alimentées par des images, elles profitèrent rapidement de la technologie OCR pour indexer les collections imprimées afin d’améliorer périmètre et performance du service de recherche d’information offert aux utilisateurs. Mais l’accès aux ressources iconographiques n’a pas connu les mêmes progrès et ces dernières demeurent dans l’ombre : indexation manuelle lacunaire, hétérogène et non viable à grande échelle ; silos documentaires par genre iconographique ; recherche par le contenu (CBIR, content-based image retrieval) encore peu opérationnelle sur les collections patrimoniales. Aujourd’hui, il serait pourtant possible de mieux valoriser ces ressources, en particulier en exploitant les énormes volumes d’OCR produits durant les deux dernières décennies (tant comme descripteur textuel que pour l’identification automatique des illustrations imprimées). Et ainsi mettre en valeur ces gravures, dessins, photographies, cartes, etc. pour leur valeur propre mais aussi comme point d’entrée dans les collections, en favorisant découverte et rebond de document en document, de collection à collection. Cet article décrit une approche ETL (extract-transform-load) appliquée aux images d’une bibliothèque numérique à vocation encyclopédique : identifier et extraire l’iconographie partout où elle se trouve (dans les collections image mais aussi dans les imprimés : presse, revue, monographie) ; transformer, harmoniser et enrichir ses métadonnées descriptives grâce à des techniques d’apprentissage machine – machine learning – pour la classification et l’indexation automatiques ; charger ces données dans une application web dédiée à la recherche iconographique (ou dans d’autres services de la bibliothèque). Approche qualifiée de pragmatique à double titre, puisqu’il s’agit de valoriser des ressources numériques existantes et de mettre à profit des technologies (quasiment) mâtures.
Hou, Jun. "Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79206/1/Jun_Hou_Thesis.pdf.
Повний текст джерелаWiklund-Hörnqvist, Carola. "Brain-based teaching : behavioral and neuro-cognitive evidence for the power of test-enhanced learning." Doctoral thesis, Umeå universitet, Institutionen för psykologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-96395.
Повний текст джерелаKühnlein, Meike [Verfasser], and Thomas [Akademischer Betreuer] Nauss. "A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe / Meike Kühnlein. Betreuer: Thomas Nauss." Marburg : Philipps-Universität Marburg, 2014. http://d-nb.info/1064097758/34.
Повний текст джерелаMinelli, Michele. "Fully homomorphic encryption for machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE056/document.
Повний текст джерелаFully homomorphic encryption enables computation on encrypted data without leaking any information about the underlying data. In short, a party can encrypt some input data, while another party, that does not have access to the decryption key, can blindly perform some computation on this encrypted input. The final result is also encrypted, and it can be recovered only by the party that possesses the secret key. In this thesis, we present new techniques/designs for FHE that are motivated by applications to machine learning, with a particular attention to the problem of homomorphic inference, i.e., the evaluation of already trained cognitive models on encrypted data. First, we propose a novel FHE scheme that is tailored to evaluating neural networks on encrypted inputs. Our scheme achieves complexity that is essentially independent of the number of layers in the network, whereas the efficiency of previously proposed schemes strongly depends on the topology of the network. Second, we present a new technique for achieving circuit privacy for FHE. This allows us to hide the computation that is performed on the encrypted data, as is necessary to protect proprietary machine learning algorithms. Our mechanism incurs very small computational overhead while keeping the same security parameters. Together, these results strengthen the foundations of efficient FHE for machine learning, and pave the way towards practical privacy-preserving deep learning. Finally, we present and implement a protocol based on homomorphic encryption for the problem of private information retrieval, i.e., the scenario where a party wants to query a database held by another party without revealing the query itself
Lakshmanan, Muthukumar S. "Using effective information searching skills to solve problems." Phd thesis, Australia : Macquarie University, 2009. http://hdl.handle.net/1959.14/42606.
Повний текст джерелаThesis (PhD)--Macquarie University, Australian Centre for Educational Studies, School of Education, 2009.
Bibliography: p. 268-283.
Introduction -- Review of the literature -- Methods and procedures -- Pre-intervention qualitative data analysis & discussion of findings -- Intervention -- Post-intervention qualitative data analysis & discussions of findings -- Post-intervention quantitative data analysis & discussions of findings -- Conclusions.
Problem-based learning (PBL) is an instructional approach that is organized around the investigation and resolution of problems. Problems are neither uniform nor similar. Jonassen (1998, 2000) in his design theory of problem solving has categorized problems into two broad types - well-structured and ill-structured. He has also described a host of mediating skills that impact problem solving outcomes. However, this list of skills is not exhaustive and in view of the utility of the Internet as an informational repository, this study examined the need for effective information searching skills to be included in this list. -- This study was aimed at studying how students solve well and ill structured problems and how different Internet information seeking strategies can be used to engage in problem solving. This study devised and empirically tested the efficacy of an interventionist conceptual model that maps the application of different information seeking techniques to successfully resolving well and ill structured problem types. The intervention helps to better understand the influence of information searching skills on problem solving performance and the various problem solving strategies students can adopt in approaching problem solving. The contrasting patterns of navigational path movements taken by students in seeking information to resolve ill and well structured problems were also investigated. -- A mixed methodology research design, involving a mix of quantitative and qualitative approaches was used in this study. The research site was a polytechnic in Singapore that has implemented problem-based learning in its curriculum design. A first year class of 25 students were the sample population who participated in this study. Six problems from the curriculum were chosen for this study - three well-structured and another three ill-structured problems. -- The research findings of this study inform that information searching skills indeed play an important role in problem solving. The findings affirm the need for students to be systematically instructed in the skills of information searching to be aware of the complexities involved in information seeking and accomplish desired problem solving goals. This study has also shown that well and ill structured problems demand different cognitive and information seeking capabilities. Well-structured problems are easily solved and come with singular correct answers. The information searching necessary for solving well-structured problems is constrained and readily manageable. Thus, students only have to be acquainted with fundamental information searching skills to solve well-structured problems. On the other hand, ill-structured problems are messy and contain a number of unknown elements. There are no easy prototypic solutions. Subsequently, the information needs of ill-structured problems are usually complex, multi-disciplinary and expansive. Hence, students have to be trained to apply a more advanced set of information searching skills in resolving ill-structured problems.
Mode of access: World Wide Web.
xiv, 283 p. ill
Zeng, Kaiman. "Next Generation of Product Search and Discovery." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2312.
Повний текст джерелаEyorokon, Vahid. "Measuring Goal Similarity Using Concept, Context and Task Features." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091.
Повний текст джерелаGorisse, David. "Passage à l’échelle des méthodes de recherche sémantique dans les grandes bases d’images." Thesis, Cergy-Pontoise, 2010. http://www.theses.fr/2010CERG0519/document.
Повний текст джерелаIn this last decade, would the digital revolution and its ancillary consequence of a massive increases in digital picture quantities. The database size grow much faster than the processing capacity of computers. The current search engine which conceived for small data volumes do not any more allow to make searches in these new corpus with acceptable response times for users.In this thesis, we propose scalable content-based image retrieval engines.At first, we considered automatic search engines where images are indexed with global histograms. Secondly, we were interested in more sophisticated engines allowing to improve the search quality by working with bag of feature. In a last time, we proposed a strategy to reduce the complexity of interactive search engines. These engines allow to improve the results by using labels which the users supply to the system during the search sessions
Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.
Повний текст джерелаPatterson, William Robert David. "Introspective techniques for maintaining retrieval knowledge in case-base reasoning." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365937.
Повний текст джерелаSoltan-Zadeh, Yasaman. "Improved rule-based document representation and classification using genetic programming." Thesis, Royal Holloway, University of London, 2011. http://repository.royalholloway.ac.uk/items/479a1773-779b-8b24-b334-7ed485311abe/8/.
Повний текст джерелаBergqvist, 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.
Повний текст джерелаThe 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.
Désoyer, Adèle. "Appariement de contenus textuels dans le domaine de la presse en ligne : développement et adaptation d'un système de recherche d'information." Thesis, Paris 10, 2017. http://www.theses.fr/2017PA100119/document.
Повний текст джерелаThe goal of this thesis, conducted within an industrial framework, is to pair textual media content. Specifically, the aim is to pair on-line news articles to relevant videos for which we have a textual description. The main issue is then a matter of textual analysis, no image or spoken language analysis was undertaken in the present study. The question that arises is how to compare these particular objects, the texts, and also what criteria to use in order to estimate their degree of similarity. We consider that one of these criteria is the topic similarity of their content, in other words, the fact that two documents have to deal with the same topic to form a relevant pair. This problem fall within the field of information retrieval (ir) which is the main strategy called upon in this research. Furthermore, when dealing with news content, the time dimension is of prime importance. To address this aspect, the field of topic detection and tracking (tdt) will also be explored.The pairing system developed in this thesis distinguishes different steps which complement one another. In the first step, the system uses natural language processing (nlp) methods to index both articles and videos, in order to overcome the traditionnal bag-of-words representation of texts. In the second step, two scores are calculated for an article-video pair: the first one reflects their topical similarity and is based on a vector space model; the second one expresses their proximity in time, based on an empirical function. At the end of the algorithm, a classification model learned from manually annotated document pairs is used to rank the results.Evaluation of the system's performances raised some further questions in this doctoral research. The constraints imposed both by the data and the specific need of the partner company led us to adapt the evaluation protocol traditionnal used in ir, namely the cranfield paradigm. We therefore propose an alternative solution for evaluating the system that takes all our constraints into account
Bellafqira, Reda. "Chiffrement homomorphe et recherche par le contenu sécurisé de données externalisées et mutualisées : Application à l'imagerie médicale et l'aide au diagnostic." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0063.
Повний текст джерелаCloud computing has emerged as a successful paradigm allowing individuals and companies to store and process large amounts of data without a need to purchase and maintain their own networks and computer systems. In healthcare for example, different initiatives aim at sharing medical images and Personal Health Records (PHR) in between health professionals or hospitals with the help of the cloud. In such an environment, data security (confidentiality, integrity and traceability) is a major issue. In this context that these thesis works, it concerns in particular the securing of Content Based Image Retrieval (CBIR) techniques and machine learning (ML) which are at the heart of diagnostic decision support systems. These techniques make it possible to find similar images to an image not yet interpreted. The goal is to define approaches that can exploit secure externalized data and enable a cloud to provide a diagnostic support. Several mechanisms allow the processing of encrypted data, but most are dependent on interactions between different entities (the user, the cloud or a trusted third party) and must be combined judiciously so as to not leak information. During these three years of thesis, we initially focused on securing an outsourced CBIR system under the constraint of no interaction between the users and the service provider (cloud). In a second step, we have developed a secure machine learning approach based on multilayer perceptron (MLP), whose learning phase can be outsourced in a secure way, the challenge being to ensure the convergence of the MLP. All the data and parameters of the model are encrypted using homomorphic encryption. Because these systems need to use information from multiple sources, each of which outsources its encrypted data under its own key, we are interested in the problem of sharing encrypted data. A problem known by the "Proxy Re-Encryption" (PRE) schemes. In this context, we have proposed the first PRE scheme that allows both the sharing and the processing of encrypted data. We also worked on watermarking scheme over encrypted data in order to trace and verify the integrity of data in this shared environment. The embedded message is accessible whether or not the image is encrypted and provides several services
Pereira, Silvio Moreto. "Caracterização de imagens de úlceras dermatológicas para indexação e recuperação por conteúdo." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/82/82131/tde-08012013-110054/.
Повний текст джерелаSkin ulcers are caused due to deficiency in the bloodstream. The diagnosis is made by a visual analysis of the affected area. Quantification of color distribution of the lesion by image processing techniques can aid in the characterization and response to treatment. The image processing steps involves skin ulcers related to segmentation, characterization and indexing. This analysis is important for classification, image retrieval and similar tracking the evolution of an injury. This project presents a study of segmentation techniques and characterization of color images of dermatological skin ulcers, based on the color models RGB, HSV, L*a*b* and L*u*v*, using their components in the extraction of texture and color information. Were used Machine Learning techniques, mathematical algorithms for segmentation and extraction of attributes, using a database containing 172 images in two versions. In recovery tests were used different distance metrics for performance evaluation and techniques of features selection. The results show good potential to support the diagnosis and monitoring of treatment progress with values up to 75% precision in recovery techniques, 0.9 area under the curve receiver-operating-characteristic) in classification, and 0.04 mean square error between the color composition of the automatically segmented image and the manually segmented image. In tests utilizing feature selection was observed a decrease in precision values of image retrieval (60%) and similar values in the classification\'s tests (0.85).
Westerdahl, Simon, and Larsson Fredrik Lemón. "Optimization for search engines based on external revision database." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-21000.
Повний текст джерелаKotevska, Olivera. "Learning based event model for knowledge extraction and prediction system in the context of Smart City." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM005/document.
Повний текст джерелаBillions of “things” connected to the Internet constitute the symbiotic networks of communication devices (e.g., phones, tablets, and laptops), smart appliances (e.g., fridge, coffee maker and so forth) and networks of people (e.g., social networks). So, the concept of traditional networks (e.g., computer networks) is expanding and in future will go beyond it, including more entities and information. These networks and devices are constantly sensing, monitoring and generating a vast amount of data on all aspects of human life. One of the main challenges in this area is that the network consists of “things” which are heterogeneous in many ways, the other is that their state of the interconnected objects is changing over time, and there are so many entities in the network which is crucial to identify their interdependency in order to better monitor and predict the network behavior. In this research, we address these problems by combining the theory and algorithms of event processing with machine learning domains. Our goal is to propose a possible solution to better use the information generated by these networks. It will help to create systems that detect and respond promptly to situations occurring in urban life so that smart decision can be made for citizens, organizations, companies and city administrations. Social media is treated as a source of information about situations and facts related to the users and their social environment. At first, we tackle the problem of identifying the public opinion for a given period (year, month) to get a better understanding of city dynamics. To solve this problem, we proposed a new algorithm to analyze complex and noisy textual data such as Twitter messages-tweets. This algorithm permits an automatic categorization and similarity identification between event topics by using clustering techniques. The second challenge is combing network data with various properties and characteristics in common format that will facilitate data sharing among services. To solve it we created common event model that reduces the representation complexity while keeping the maximum amount of information. This model has two major additions: semantic and scalability. The semantic part means that our model is underlined with an upper-level ontology that adds interoperability capabilities. While the scalability part means that the structure of the proposed model is flexible in adding new entries and features. We validated this model by using complex event patterns and predictive analytics techniques. To deal with the dynamic environment and unexpected changes we created dynamic, resilient network model. It always chooses the optimal model for analytics and automatically adapts to the changes by selecting the next best model. We used qualitative and quantitative approach for scalable event stream selection, that narrows down the solution for link analysis, optimal and alternative best model. It also identifies efficient relationship analysis between data streams such as correlation, causality, similarity to identify relevant data sources that can act as an alternative data source or complement the analytics process
Guillaumin, Matthieu. "Données multimodales pour l'analyse d'image." Phd thesis, Grenoble, 2010. http://tel.archives-ouvertes.fr/tel-00522278/en/.
Повний текст джерелаZhou, 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.
Повний текст джерелаZhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Queensland University of Technology, 2008. http://eprints.qut.edu.au/29350/.
Повний текст джерелаLaurier, Cyril François. "Automatic Classification of musical mood by content-based analysis." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/51582.
Повний текст джерелаEn esta tesis, nos centramos en la clasificación automática de música a partir de la detección de la emoción que comunica. Primero, estudiamos cómo los miembros de una red social utilizan etiquetas y palabras clave para describir la música y las emociones que evoca, y encontramos un modelo para representar los estados de ánimo. Luego, proponemos un método de clasificación automática de emociones. Analizamos las contribuciones de descriptores de audio y cómo sus valores están relacionados con los estados de ánimo. Proponemos también una versión multimodal de nuestro algoritmo, usando las letras de canciones. Finalmente, después de estudiar la relación entre el estado de ánimo y el género musical, presentamos un método usando la clasificación automática por género. A modo de recapitulación conceptual y algorítmica, proponemos una técnica de extracción de reglas para entender como los algoritmos de aprendizaje automático predicen la emoción evocada por la música
Dzhambazov, Georgi. "Knowledge-based probabilistic modeling for tracking lyrics in music audio signals." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404681.
Повний текст джерелаLa tesi aquí presentada proposa metodologies d’aprenentatge automàtic i processament de senyal per alinear automàticament el text d’una cançó amb el seu corresponent enregistrament d’àudio. La recerca duta a terme s’engloba en l’ampli camp de l’extracció d’informació musical (Music Information Retrieval o MIR). Dins aquest context la tesi pretén millorar algunes de les metodologies d’última generació del camp introduint coneixement específic de l’àmbit. L’objectiu d’aquest treball és dissenyar models que siguin capaços de detectar en la senyal d’àudio l’aspecte seqüencial d’un element particular dels textos musicals; els fonemes. Podem entendre la música com la composició de diversos elements entre els quals podem trobar el text. Els models que construïm tenen en compte el context complementari del text. El context són tots aquells aspectes musicals que complementen el text, dels quals hem utilitzat en aquest tesi: la estructura de la composició musical, la estructura de les frases melòdiques i els accents rítmics. Des d’aquesta prespectiva analitzem no només les característiques acústiques de baix nivell, que representen el timbre musical dels fonemes, sinó també les característiques d’alt nivell en les quals es fa patent el context complementari. En aquest treball proposem models probabilístics específics que representen com les transicions entre fonemes consecutius de veu cantanda es veuen afectats per diversos aspectes del context complementari. El context complementari que tractem aquí es desenvolupa en el temps en funció de les característiques particulars de cada tradició musical. Per tal de modelar aquestes característiques hem creat corpus i conjunts de dades de dues tradicions musicals que presenten una gran riquesa en aquest aspectes; la música de l’opera de Beijing i la música makam turc-otomana. Les dades són de diversos tipus; enregistraments d’àudio, partitures musicals i metadades. Des d’aquesta prespectiva els models proposats poden aprofitar-se tant de les dades en si mateixes com del coneixement específic de la tradició musical per a millorar els resultats de referència actuals. Com a resultat de referència prenem un reconeixedor de fonemes basat en models ocults de Markov (Hidden Markov Models o HMM), una metodologia abastament emprada per a detectar fonemes tant en la veu cantada com en la parlada. Presentem millores en els processos comuns dels reconeixedors de fonemes actuals, ajustant-los a les característiques de les tradicions musicals estudiades. A més de millorar els resultats de referència també dissenyem models probabilistics basats en xarxes dinàmiques de Bayes (Dynamic Bayesian Networks o DBN) que respresenten la relació entre la transició dels fonemes i el context complementari. Hem creat dos models diferents per dos aspectes del context complementari; la estructura de la frase melòdica (alt nivell) i la estructura mètrica (nivell subtil). En un dels models explotem el fet que la duració de les síl·labes depén de la seva posició en la frase melòdica. Obtenim aquesta informació sobre les frases musical de la partitura i del coneixement específic de la tradició musical. En l’altre model analitzem com els atacs de les notes vocals, estimats directament dels enregistraments d’àudio, influencien les transicions entre vocals i consonants consecutives. A més també proposem com detectar les posicions temporals dels atacs de les notes en les frases melòdiques a base de localitzar simultàniament els accents en un cicle mètric musical. Per tal d’evaluar el potencial dels mètodes proposats utlitzem la tasca específica d’alineament de text amb àudio. Cada model proposat millora la precisió de l’alineament en comparació als resultats de referència, que es basen exclusivament en les característiques acústiques tímbriques dels fonemes. D’aquesta manera validem la nostra hipòtesi de que el coneixement del context complementari ajuda a la detecció automàtica de text musical, especialment en el cas de veu cantada amb acompanyament instrumental. Els resultats d’aquest treball no consisteixen només en metodologies teòriques i dades, sinó també en eines programàtiques específiques que han sigut integrades a Dunya, un paquet d’eines creat en el context del projecte de recerca CompMusic, l’objectiu del qual és promoure l’anàlisi computacional de les músiques del món. Gràcies a aquestes eines demostrem també que les metodologies desenvolupades es poden fer servir per a altres aplicacions en el context de la educació musical o la escolta musical enriquida.
(6996329), Garrett M. O'Day. "Improving Problem Solving with Retrieval-Based Learning." Thesis, 2019.
Знайти повний текст джерелаRecent research asserts that the mnemonic benefits gained from retrieval-based learning vanish for complex materials. Subsequently, it is recommended that students study worked examples when learning about complex, problem-centered tasks. The experiments that have evaluated the effectiveness of studying worked examples tend to overlook the mental processing that students engage in when completing retrieval-based learning activities. In contrast, theories of transfer-appropriate processing emphasize the importance of compatibility between the cognitive processing required by the test and the cognitive processing that is activated during learning. For learners to achieve optimal test performance, according to transfer-appropriate processing, they need to study in such a way that they are engaging in the same mental processing that will be required of them when tested. This idea was used to generate testable predictions that compete against the claim that the retrieval practice effect disappears for complex materials, and these competing predictions were evaluated in three experiments that required students to learn about the Poisson probability distribution.
In Experiment 1, students learned the general procedure for how to solve these problems by either repeatedly recalling the procedural steps or by simply studying them. The retrieval practice condition produced better memory for the procedure on an immediate test compared to the study only condition. In Experiment 2, students engaged in the same learning activities as Experiment 1, but the test focused on their problem- solving ability. Students who practiced retrieval of the procedural steps experienced no benefit on the problem-solving test compared to the study only condition. In Experiment 3, students learned to solve Poisson probability problems by studying four worked examples, by studying one worked example and solving three practice problems, or by studying one worked example and solving three practice problems with feedback. Students were tested on their problem-solving ability one week later. The problem- solving learning activities outperformed the worked example condition on the final problem-solving test. Taken together, the results demonstrate a pronounced retrieval practice effect but only when the retrieval-based learning activities necessitated the same mental processing that was required during the final assessment, providing support for the transfer-appropriate processing account.
Chou, Yu-Sheng, and 周佑昇. "Personalized Face Retrieval based on Multi-Kernel Learning." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/14222127089871615899.
Повний текст джерела中原大學
資訊工程研究所
102
In recent years, attribute-based face image retrieval has become a hot research topic due to the explosive growth of social media. Semantic visual attributes are pre-trained and combined to retrieve specific face images. However, just like an image cannot be described by keywords completely, it is impossible to describe a face image by limited attributes. Therefore, we propose a personalized face image retrieval scheme based on Generalized Multiple Kernel Learning (GMKL) in this paper. Each face image is first aligned by Constrained Local Model (CLM) and landmarks are extracted for locating facial parts automatically. The local features extracted from different facial parts are then modeled as the base-kernels in GMKL. After learning the kernel weights from the training set that selected by a user, face image retrieval can be achieved without pre-training attributes. Experimental results show that our method is reliable and efficient on LFW dataset using only tens training samples.
Hsu, Ching-Yu, and 許靖雨. "Information Retrieval Technology for Internet Based Learning Evaluation." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/02340663199595511023.
Повний текст джерела國立臺灣大學
土木工程學研究所
92
This study presents application of Information Retrieval (IR) technology for internet-based learning (IBL) evaluation. The objective is to support IBL, which is getting important due to the ubiquity of the internet. The learning evaluation of students are based on a “instruction assisting knowledge set” which consists of key words of learning materials. Acorrding to “learning assisting knowledge set”, we use IR technology, such as similarity evaluation and query expansion, to analyze students’ performance, especially for their learning universality. A two-week case study is conducted on junior and senior students in the department of Civil Engineering. The results show that the evaluation obtained from the system in good agreement with that of the instructor. The study that verifies the applicability of using IR technology to analyze students’ behaviors. As a result, instructors could use the analysis to find out students’ learning problem and provide appropriate guidance.