Дисертації з теми "Multi-view machine learning"
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Labroski, Aleksandar. "Multi-view versus single-view machine learning for disease diagnosis in primary healthcare." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235533.
Повний текст джерелаArbetet som presenteras i denna rapport beaktar och jämför två olika metoder för maskininlärning för att lösa problemet med prognos för sjukdomsdiagnos i primärvården: single-view och multi-view maskininlärning. I synnerhet avser problemet med sjukdomsdiagnos prediktion av en (möjlig) diagnos för en given patient, baserat på dennes tidigare medicinska historia. Problemområdet är omfattande, i synnerhet med tanke på att det finns över 14 400 unika möjliga diagnoser (grupperade i 22 högkvalitativa kategorier) som kan betraktas som förutsägbara. Tillvägagångssättet i detta arbete betraktar kategorierna i hög-nivå och försöker använda de två olika maskininlärningsteknikerna för att komma nära en optimal lösning på problemet. Multi-view maskininlärningsparadigmet valdes som ett tillvägagångssätt som kan förbättra prediktiv prestanda för klassifikationer i inställningar där vi har flera heterogena datakällor (olika visningar av samma data), vilket är det exakta fallet här. För att jämföra single-view och multi-view maskininlärning paradigmerna (baserat på begreppet övervakat lärande), är flera olika experiment utformade som undersöker det möjliga lösningsutrymmet under varje paradigm. Arbetet berör noga andra koncept för maskininlärning, som ensembleinlärning, samlad generalisering och dimensioneringsreduktionsbaserat lärande. Som vi kan se visar resultaten att multi-view samlad generalisering är ett kraftfullt paradigm som kan förbättra den prediktiva prestandan avsevärt i en övervakad inlärningsinställning. De olika modellernas prestanda utvärderades med hjälp av F1-poäng och vi har kunnat observera en genomsnittlig ökning av prestanda på 0,04 och en maximal ökning av 0.114 F1 poäng. Resultaten visar också att tillvägagångssättet för multi-view stacked ensemblelärande är särskilt väl lämpat som en brusreduceringsteknik och fungerar bra i fall där funktionsdata förväntas innehålla en anmärkningsvärd mängd brus. Detta kan vara mycket fördelaktigt och av intresse för projekt där funktioner inte manuellt väljs av domänexperter.
Byun, Byungki. "On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43597.
Повний текст джерелаZantedeschi, Valentina. "A Unified View of Local Learning : Theory and Algorithms for Enhancing Linear Models." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES055/document.
Повний текст джерелаIn Machine Learning field, data characteristics usually vary over the space: the overall distribution might be multi-modal and contain non-linearities.In order to achieve good performance, the learning algorithm should then be able to capture and adapt to these changes. Even though linear models fail to describe complex distributions, they are renowned for their scalability, at training and at testing, to datasets big in terms of number of examples and of number of features. Several methods have been proposed to take advantage of the scalability and the simplicity of linear hypotheses to build models with great discriminatory capabilities. These methods empower linear models, in the sense that they enhance their expressive power through different techniques. This dissertation focuses on enhancing local learning approaches, a family of techniques that infers models by capturing the local characteristics of the space in which the observations are embedded. The founding assumption of these techniques is that the learned model should behave consistently on examples that are close, implying that its results should also change smoothly over the space. The locality can be defined on spatial criteria (e.g. closeness according to a selected metric) or other provided relations, such as the association to the same category of examples or a shared attribute. Local learning approaches are known to be effective in capturing complex distributions of the data, avoiding to resort to selecting a model specific for the task. However, state of the art techniques suffer from three major drawbacks: they easily memorize the training set, resulting in poor performance on unseen data; their predictions lack of smoothness in particular locations of the space;they scale poorly with the size of the datasets. The contributions of this dissertation investigate the aforementioned pitfalls in two directions: we propose to introduce side information in the problem formulation to enforce smoothness in prediction and attenuate the memorization phenomenon; we provide a new representation for the dataset which takes into account its local specificities and improves scalability. Thorough studies are conducted to highlight the effectiveness of the said contributions which confirmed the soundness of their intuitions. We empirically study the performance of the proposed methods both on toy and real tasks, in terms of accuracy and execution time, and compare it to state of the art results. We also analyze our approaches from a theoretical standpoint, by studying their computational and memory complexities and by deriving tight generalization bounds
Xie, Zhiyuan. "Effect of Enhancement on Convolutional Neural Network Based Multi-view Object Classification." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1522937516903222.
Повний текст джерелаSeifi, Farid. "Improving Classification and Attribute Clustering: An Iterative Semi-supervised Approach." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32140.
Повний текст джерелаLi, Rui [Verfasser], Burkhard [Akademischer Betreuer] [Gutachter] Rost, and Stefan [Gutachter] Kramer. "Data Mining and Machine Learning Methods for High-dimensional Patient Data in Dementia Research: Voxel Features Mining, Subgroup Discovery and Multi-view Learning / Rui Li ; Gutachter: Burkhard Rost, Stefan Kramer ; Betreuer: Burkhard Rost." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1125018224/34.
Повний текст джерелаSoares, Matheus Victor Brum. "Aprendizado de máquina parcialmente supervisionado multidescrição para realimentação de relevância em recuperação de informação na WEB." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-03092009-135403/.
Повний текст джерелаAs nowadays the WEB is the most common source of information, it is very important to find reliable and efficient methods to retrieve this information. However, the WEB is a highly volatile and heterogeneous information source, thus keyword based querying may not be the best approach when few information is given. This is due to the fact that different users with different needs may want distinct information, although related to the same keyword query. The process of relevance feedback makes it possible for the user to interact actively with the search engine. The main idea is that after performing an initial search in the WEB, the process enables the user to indicate, among the retrieved sites, a small number of the ones considered relevant or irrelevant according with his/her required information. The users preferences can then be used to rearrange sites returned in the initial search, so that relevant sites are ranked first. As in most cases a search returns a large amount of WEB sites which fits the keyword query, this is an ideal situation to use partially supervised machine learning algorithms. This kind of learning algorithms require a small number of labeled examples, and a large number of unlabeled examples. Thus, based on the assumption that the use of partially supervised learning is appropriate to induce a classifier that can be used as a filter for relevance feedback in WEB information retrieval, the aim of this work is to explore the use of a partially supervised machine learning algorithm, more specifically, one that uses multi-description data, in order to assist the WEB search. To this end, a computational tool called C-SEARCH, which performs the reordering of the searched results using the users feedback, has been implemented. Experimental results show that in cases where the keyword query is generic and there is a clear distinction between relevant and irrelevant sites, which is recognized by the user, the system can achieve good results
Koco, Sokol. "Méthodes ensembliste pour des problèmes de classification multi-vues et multi-classes avec déséquilibres." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4101/document.
Повний текст джерелаNowadays, in many fields, such as bioinformatics or multimedia, data may be described using different sets of features, also called views. For a given classification task, we distinguish two types of views:strong views, which are suited for the task, and weak views suited for a (small) part of the task; in multi-class learning, a view can be strong with respect to some (few) classes and weak for the rest of the classes: these are imbalanced views. The works presented in this thesis fall in the supervised learning setting and their aim is to address the problem of multi-view learning under strong, weak and imbalanced views, regrouped under the notion of uneven views. The first contribution of this thesis is a multi-view learning algorithm based on the same framework as AdaBoost.MM. The second part of this thesis proposes a unifying framework for imbalanced classes supervised methods (some of the classes are more represented than others). In the third part of this thesis, we tackle the uneven views problem through the combination of the imbalanced classes framework and the between-views cooperation used to take advantage of the multiple views. In order to test the proposed methods on real-world data, we consider the task of phone calls classifications, which constitutes the subject of the ANR DECODA project. Each part of this thesis deals with different aspects of the problem
Matsubara, Edson Takashi. "O algoritmo de aprendizado semi-supervisionado co-training e sua aplicação na rotulação de documentos." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19082004-092311/.
Повний текст джерелаIn Machine Learning, the supervised approach usually requires a large number of labeled training examples to learn accurately. However, labeling is often manually performed, making this process costly and time-consuming. By contrast, unlabeled examples are often inexpensive and easier to obtain than labeled examples. This is especially true for text classification tasks involving on-line data sources, such as web pages, email and scientific papers. Text classification is of great practical importance today given the massive volume of online text available. Semi-supervised learning, a relatively new area in Machine Learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labeled data whenever only a small set of labeled examples is available. This work describes the semi-supervised learning algorithm co-training, which requires a partitioned description of each example into two distinct views. It should be observed that the two different views required by co-training can be easily obtained from textual documents through pre-processing. In this works, several extensions of co-training algorithm have been implemented. Furthermore, we have also implemented a computational environment for text pre-processing, called PreTexT, in order to apply the co-training algorithm to text classification problems. Experimental results using co-training on three data sets are described. Two data sets are related to text classification and the other one to web-page classification. Results, which range from excellent to poor, show that co-training, similarly to other semi-supervised learning algorithms, is affected by modelling assumptions in a rather complicated way.
Twinanda, Andru Putra. "Vision-based approaches for surgical activity recognition using laparoscopic and RBGD videos." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD005/document.
Повний текст джерелаThe main objective of this thesis is to address the problem of activity recognition in the operating room (OR). Activity recognition is an essential component in the development of context-aware systems, which will allow various applications, such as automated assistance during difficult procedures. Here, we focus on vision-based approaches since cameras are a common source of information to observe the OR without disrupting the surgical workflow. Specifically, we propose to use two complementary video types: laparoscopic and OR-scene RGBD videos. We investigate how state-of-the-art computer vision approaches perform on these videos and propose novel approaches, consisting of deep learning approaches, to carry out the tasks. To evaluate our proposed approaches, we generate large datasets of recordings of real surgeries. The results demonstrate that the proposed approaches outperform the state-of-the-art methods in performing surgical activity recognition on these new datasets
Sublemontier, Jacques-Henri. "Classification non supervisée : de la multiplicité des données à la multiplicité des analyses." Phd thesis, Université d'Orléans, 2012. http://tel.archives-ouvertes.fr/tel-00801555.
Повний текст джерелаBraga, Ígor Assis. "Aprendizado semissupervisionado multidescrição em classificação de textos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02062010-160019/.
Повний текст джерелаSemi-supervised learning algorithms learn from a combination of both labeled and unlabeled data. Thus, they can be applied in domains where few labeled examples and a vast amount of unlabeled examples are available. Furthermore, semi-supervised learning algorithms may achieve a better performance than supervised learning algorithms trained on the same few labeled examples. A powerful approach to semi-supervised learning, called multi-view learning, can be used whenever the training examples are described by two or more disjoint sets of attributes. Text classification is a domain in which semi-supervised learning algorithms have shown some success. However, multi-view semi-supervised learning has not yet been well explored in this domain despite the possibility of describing textual documents in a myriad of ways. The aim of this work is to analyze the effectiveness of multi-view semi-supervised learning in text classification using unigrams and bigrams as two distinct descriptions of text documents. To this end, we initially consider the widely adopted CO-TRAINING multi-view algorithm and propose some modifications to it in order to deal with the problem of contention points. We also propose the COAL algorithm, which further improves CO-TRAINING by incorporating active learning as a way of dealing with contention points. A thorough experimental evaluation of these algorithms was conducted on real text data sets. The results show that the COAL algorithm, using unigrams as one description of text documents and bigrams as another description, achieves significantly better performance than a single-view semi-supervised algorithm. Taking into account the good results obtained by COAL, we conclude that the use of unigrams and bigrams as two distinct descriptions of text documents can be very effective
Robbeloth, Michael Christopher. "Recognition of Incomplete Objects based on Synthesis of Views Using a Geometric Based Local-Global Graphs." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1557509373174391.
Повний текст джерелаLiu, Fang. "Efficient Online Learning with Bandit Feedback." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587680990430268.
Повний текст джерелаTumati, Saini. "A Combined Approach to Handle Multi-class Imbalanced Data and to Adapt Concept Drifts using Machine Learning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623240328088387.
Повний текст джерелаPatel, Neel R. "CHARACTERIZING GLOBAL REGULATORY PATTERNS OF TRANSCRIPTION FACTORS ON SYSTEMS-WIDE SCALE USING MULTI-OMICS DATASETS AND MACHINE LEARNING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1626284802198267.
Повний текст джерелаWalton, Ashley E. "Multi-scaled assessment for predicting pain experience in adolescents with Sickle Cell Disease." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522332374293073.
Повний текст джерелаBisig, Caleb R. "Modular Decentralized Genetic Fuzzy Control for Multi-UAV Slung Payloads." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617106491512366.
Повний текст джерелаChintalapati, Veera Venkata Tarun Kartik. "Multi-Vehicle Path Following and Adversarial Agent Detection in Constrained Environments." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751238253121.
Повний текст джерелаMaguluri, Naga Sai Nikhil. "Multi-Class Classification of Textual Data: Detection and Mitigation of Cheating in Massively Multiplayer Online Role Playing Games." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1494248022049882.
Повний текст джерелаHulbert, Sarah Marie HULBERT. "Biophysical Approaches for the Multi-System Analysis of Neural Control of Movement and Neurologic Rehabilitation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534678369235538.
Повний текст джерелаLi, Yichao. "Algorithmic Methods for Multi-Omics Biomarker Discovery." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533.
Повний текст джерелаCurnalia, James W. "The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.
Повний текст джерелаPartin, Michael. "Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567073723628721.
Повний текст джерелаTaslimitehrani, Vahid. "Contrast Pattern Aided Regression and Classification." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1459377694.
Повний текст джерелаSiddiqui, Mohammad Faridul Haque. "A Multi-modal Emotion Recognition Framework Through The Fusion Of Speech With Visible And Infrared Images." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556459232937498.
Повний текст джерелаLiu, Yuzhou. "Deep CASA for Robust Pitch Tracking and Speaker Separation." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1566179636974186.
Повний текст джерелаKarvir, Hrishikesh. "Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291.
Повний текст джерелаOLAOYE, ISRAEL A. "WATER QUALITY MODELING OF THE OLD WOMAN CREEK WATERSHED, OHIO, UNDER THE INFLUENCE OF CLIMATE CHANGE TO YEAR 2100." Kent State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=kent1605955492844115.
Повний текст джерелаFallahtafti, Alireza. "Developing Risk-Minimizing Vehicle Routing Problem for Transportation of Valuables: Models and Algorithms." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1627568962315484.
Повний текст джерелаJin, Gaole. "On surrogate supervision multi-view learning." Thesis, 2012. http://hdl.handle.net/1957/37997.
Повний текст джерелаGraduation date: 2013
"Multi-view machine learning for integration of brain imaging and (epi)genomics data." Tulane University, 2021.
Знайти повний текст джерелаÅleskog, Christoffer. "Graph-based Multi-view Clustering for Continuous Pattern Mining." Thesis, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21850.
Повний текст джерелаKuo, Wei-Yuan, and 郭瑋元. "A Real-time Basketball Action Recognition based on Machine Learning Algorithm in Multi-View Environment." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/et723c.
Повний текст джерела國立中央大學
通訊工程學系
105
Human action recognition has been an important research in computer vision and computer graphics. It is widely used in entertainment, sports, medical applications and surveillance system. The traditional motion capture equipment is not usually affordable for normal developer. With the reasonable price of Kinect camera, low-cost human motion recognition becomes possible. In this paper, we use multiple Kinect sensors and Kinect SDK as the tool to build our human action recognition system. This solves the problem of action recognition equipment costs. Using multiple Kinect cameras to solve the judging and correction error problems (such as self-occlusion and image noise...etc.) and using machine learning method to classified our features, it can make our recognition result with higher performance. In our methods, we also have a detection of basketball to prevent that the subject is without ball, it makes our works more reasonable. Above of all, this paper have the action recognition rate to be more than 90% in real-time usage from three of the trained behaviors, i.e. right-hand dribble, left-hand dribble, and shooting behaviors.
(9187466), Bharath Kumar Comandur Jagannathan Raghunathan. "Semantic Labeling of Large Geographic Areas Using Multi-Date and Multi-View Satellite Images and Noisy OpenStreetMap Labels." Thesis, 2020.
Знайти повний текст джерела(10157291), Yi-Yu Lai. "Relational Representation Learning Incorporating Textual Communication for Social Networks." Thesis, 2021.
Знайти повний текст джерелаJANG, JIAN JIA-CHING, and 張簡嘉慶. "Multi-View Face Recognition by Convolutionary Extreme Learning Machines." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/88fbcj.
Повний текст джерела國立高雄大學
電機工程學系碩博士班
105
Artificial Neural Network (ANN) is one of the methods for implementing the core learning engine for face image recognition. However, the difficulties in determining effective network architectures and learning weights make ANN hard to be realized for practical applications. Extreme Learning Machine(ELM) is an improved version of ANN, that employs simpler network architecture and training process for implementing learning systems in an efficient way. This study integrates ELM with several enhancements for effective face image recognition. First, convolution is used to extract the features of face images. Second, the technique of pooling is used to reduce the very high dimension of feature vectors of face images. With convolution and pooling, features and models for face image recognition can be obtained with fewer training time. Furthermore, most face recognition systems only detect the front face image as the target for recognition. In practical applications, the incorrect capturing angle of camera may result in the lost or corruption of some image features, and hence, affect the recognition accuracy. In this study, multi-view face images from different capturing angles are extracted for training multi-view face recognition models. A variety of kernels and pooling methods are tested and compared. The performance of face recognition using single-view and multi-view methods is also compared and discussed. The experimental results show that our method improves the training performance of ELM for face recognition with satisfactory recognition accuracy.