Relevant bibliographies by topics / Multi-view machine learning

Academic literature on the topic 'Multi-view machine learning'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Multi-view machine learning"

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WANG, ZHE, MINGZHE LU, ZENGXIN NIU, XIANGYANG XUE, and DAQI GAO. "COST-SENSITIVE MULTI-VIEW LEARNING MACHINE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (May 2014): 1451004. http://dx.doi.org/10.1142/s0218001414510045.

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Multi-view learning aims to effectively learn from data represented by multiple independent sets of attributes, where each set is taken as one view of the original data. In real-world application, each view should be acquired in unequal cost. Taking web-page classification for example, it is cheaper to get the words on itself (view one) than to get the words contained in anchor texts of inbound hyper-links (view two). However, almost all the existing multi-view learning does not consider the cost of acquiring the views or the cost of evaluating them. In this paper, we support that different views should adopt different representations and lead to different acquisition cost. Thus we develop a new view-dependent cost different from the existing both class-dependent cost and example-dependent cost. To this end, we generalize the framework of multi-view learning with the cost-sensitive technique and further propose a Cost-sensitive Multi-View Learning Machine named CMVLM for short. In implementation, we take into account and measure both the acquisition cost and the discriminant scatter of each view. Then through eliminating the useless views with a predefined threshold, we use the reserved views to train the final classifier. The experimental results on a broad range of data sets including the benchmark UCI, image, and bioinformatics data sets validate that the proposed algorithm can effectively reduce the total cost and have a competitive even better classification performance. The contributions of this paper are that: (1) first proposing a view-dependent cost; (2) establishing a cost-sensitive multi-view learning framework; (3) developing a wrapper technique that is universal to most multiple kernel based classifier.
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Wang, Qiang, Yong Dou, Xinwang Liu, Qi Lv, and Shijie Li. "Multi-view clustering with extreme learning machine." Neurocomputing 214 (November 2016): 483–94. http://dx.doi.org/10.1016/j.neucom.2016.06.035.

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Sun, Shiliang. "A survey of multi-view machine learning." Neural Computing and Applications 23, no. 7-8 (February 17, 2013): 2031–38. http://dx.doi.org/10.1007/s00521-013-1362-6.

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Karaaba, Mahir Faik, Lambert Schomaker, and Marco Wiering. "Machine learning for multi-view eye-pair detection." Engineering Applications of Artificial Intelligence 33 (August 2014): 69–79. http://dx.doi.org/10.1016/j.engappai.2014.04.008.

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Zhang, Yongshan, Jia Wu, Chuan Zhou, Zhihua Cai, Jian Yang, and Philip S. Yu. "Multi-View Fusion with Extreme Learning Machine for Clustering." ACM Transactions on Intelligent Systems and Technology 10, no. 5 (November 14, 2019): 1–23. http://dx.doi.org/10.1145/3340268.

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Tang, Jingjing, Dewei Li, Yingjie Tian, and Dalian Liu. "Multi-view learning based on nonparallel support vector machine." Knowledge-Based Systems 158 (October 2018): 94–108. http://dx.doi.org/10.1016/j.knosys.2018.05.036.

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Zhu, Changming, Chao Chen, Rigui Zhou, Lai Wei, and Xiafen Zhang. "A new multi-view learning machine with incomplete data." Pattern Analysis and Applications 23, no. 3 (February 11, 2020): 1085–116. http://dx.doi.org/10.1007/s10044-020-00863-y.

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Wan, Zhibin, Changqing Zhang, Pengfei Zhu, and Qinghua Hu. "Multi-View Information-Bottleneck Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 10085–92. http://dx.doi.org/10.1609/aaai.v35i11.17210.

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In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi-view representation learning model termed Collaborative Multi-View Information Bottleneck Networks (CMIB-Nets), which comprehensively explores the common latent structure and the view-specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. Specifically, our proposed model relies on the information bottleneck principle to integrate the shared representation among different views and the view-specific representation of each view, prompting the multi-view complete representation and flexibly balancing the complementarity and consistency among multiple views. We conduct extensive experiments (including clustering analysis, robustness experiment, and ablation study) on real-world datasets, which empirically show promising generalization ability and robustness compared to state-of-the-arts.
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姚, 瑞. "Semi-Supervised Learning Machine Based on Multi-View Twin Support Vector Machine." Operations Research and Fuzziology 09, no. 02 (2019): 177–88. http://dx.doi.org/10.12677/orf.2019.92021.

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Li, Yanchao, Yongli Wang, Junlong Zhou, and Xiaohui Jiang. "Robust Transductive Support Vector Machine for Multi-View Classification." Journal of Circuits, Systems and Computers 27, no. 12 (June 22, 2018): 1850185. http://dx.doi.org/10.1142/s0218126618501852.

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Semi-Supervised Learning (SSL) aims to improve the performance of models trained with a small set of labeled data and a large collection of unlabeled data. Learning multi-view representations from different perspectives of data has proved to be very effectively for improving generalization performance. However, existing semi-supervised multi-view learning methods tend to ignore the specific difficulty of different unlabeled examples, such as the outliers and noise, leading to error-prone classification. To address this problem, this paper proposes Robust Transductive Support Vector Machine (RTSVM) that introduces the margin distribution into TSVM, which is robust to the outliers and noise. Specifically, the first-order (margin mean) and second-order statistics (margin variance) are regularized into TSVM, which try to achieve strong generalization performance. Then, we impose a global similarity constraint between distinct RTSVMs each trained from one view of the data. Moreover, our algorithm runs with fast convergence by using concave–convex procedure. Finally, we validate our proposed method on a variety of multi-view datasets, and the experimental results demonstrate that our proposed algorithm is effective. By exploring large amount of unlabeled examples and being robust to the outliers and noise among different views, the generalization performance of our method show the superiority to single-view learning and other semi-supervised multi-view learning methods.
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Dissertations / Theses on the topic "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.

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The work presented in this report considers and compares two different approaches of machine learning towards solving the problem of disease diagnosis prediction in primary healthcare: single-view and multi-view machine learning. In particular, the problem of disease diagnosis prediction refers to the issue of predicting a (possible) diagnosis for a given patient based on her past medical history. The problem area is extensive, especially considering the fact that there are over 14,400 unique possible diagnoses (grouped into22 high level categories) that can be considered as prediction targets. The approach taken in this work considers the high-level categories as prediction targets and attempts to use the two different machine learning techniques towards getting close to an optimal solution of the issue. The multi-view machine learning paradigm was chosen as an approach that can improve predictive performance of classifiers in settings where we have multiple heterogeneous data sources (different views of the same data), which is exactlyt he case here. In order to compare the single-view and multi-view machine learning paradigms (based on the concept of supervised learning), several different experiments are devised which explore the possible solution space under each paradigm. The work closely touches on other machine learning concepts such as ensemble learning, stacked generalization and dimensionality reduction-based learning. As we shall see, the results show that multiview stacked generalization is a powerful paradigm that can significantly improve the predictive performance in a supervised learning setting. The different models performance was evaluated using F1 scores and we have been able to observe an average increase of performance of 0.04 and a maximum increase of 0.114 F1 score points. The findings also show that approach of multi-view stacked ensemble learning is particularly well suited as a noise reduction technique and works well in cases where the feature data is expected to contain a notable amount of noise. This can be very beneficial and of interest to projects where the features are not manually chosen by domainexperts.
Arbetet 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.
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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.

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This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having a large number of labeled images is crucial. However, the size of the training set is often limited due to the cost required for generating concept labels associated with objects in a large quantity of images. To address this issue, in this research, we propose to incrementally incorporate unlabeled samples into a learning process to enhance concept models originally learned with a small number of labeled samples. To tackle the sub-optimality problem of conventional techniques, the proposed incremental learning framework selects unlabeled samples based on an expected error reduction function that measures contributions of the unlabeled samples based on their ability to increase the modeling accuracy. To improve the convergence property of the proposed incremental learning framework, we further propose a multi-view learning approach that makes use of multiple features such as color, texture, etc., of images when including unlabeled samples. For robustness to mismatches between training and testing conditions, a discriminative learning algorithm, namely a kernelized maximal- figure-of-merit (kMFoM) learning approach is also developed. Combining individual techniques, we conduct a set of experiments on various image concept modeling problems, such as handwritten digit recognition, object recognition, and image spam detection to highlight the effectiveness of the proposed framework.
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Zantedeschi, Valentina. "A Unified View of Local Learning : Theory and Algorithms for Enhancing Linear Models." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES055/document.

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Dans le domaine de l'apprentissage machine, les caractéristiques des données varient généralement dans l'espace des entrées : la distribution globale pourrait être multimodale et contenir des non-linéarités. Afin d'obtenir de bonnes performances, l'algorithme d'apprentissage devrait alors être capable de capturer et de s'adapter à ces changements. Même si les modèles linéaires ne parviennent pas à décrire des distributions complexes, ils sont réputés pour leur passage à l'échelle, en entraînement et en test, aux grands ensembles de données en termes de nombre d'exemples et de nombre de fonctionnalités. Plusieurs méthodes ont été proposées pour tirer parti du passage à l'échelle et de la simplicité des hypothèses linéaires afin de construire des modèles aux grandes capacités discriminatoires. Ces méthodes améliorent les modèles linéaires, dans le sens où elles renforcent leur expressivité grâce à différentes techniques. Cette thèse porte sur l'amélioration des approches d'apprentissage locales, une famille de techniques qui infère des modèles en capturant les caractéristiques locales de l'espace dans lequel les observations sont intégrées.L'hypothèse fondatrice de ces techniques est que le modèle appris doit se comporter de manière cohérente sur des exemples qui sont proches, ce qui implique que ses résultats doivent aussi changer de façon continue dans l'espace des entrées. La localité peut être définie sur la base de critères spatiaux (par exemple, la proximité en fonction d'une métrique choisie) ou d'autres relations fournies, telles que l'association à la même catégorie d'exemples ou un attribut commun. On sait que les approches locales d'apprentissage sont efficaces pour capturer des distributions complexes de données, évitant de recourir à la sélection d'un modèle spécifique pour la tâche. Cependant, les techniques de pointe souffrent de trois inconvénients majeurs :ils mémorisent facilement l'ensemble d'entraînement, ce qui se traduit par des performances médiocres sur de nouvelles données ; leurs prédictions manquent de continuité dans des endroits particuliers de l'espace ; elles évoluent mal avec la taille des ensembles des données. Les contributions de cette thèse examinent les problèmes susmentionnés dans deux directions : nous proposons d'introduire des informations secondaires dans la formulation du problème pour renforcer la continuité de la prédiction et atténuer le phénomène de la mémorisation ; nous fournissons une nouvelle représentation de l'ensemble de données qui tient compte de ses spécificités locales et améliore son évolutivité. Des études approfondies sont menées pour mettre en évidence l'efficacité de ces contributions pour confirmer le bien-fondé de leurs intuitions. Nous étudions empiriquement les performances des méthodes proposées tant sur des jeux de données synthétiques que sur des tâches réelles, en termes de précision et de temps d'exécution, et les comparons aux résultats de l'état de l'art. Nous analysons également nos approches d'un point de vue théorique, en étudiant leurs complexités de calcul et de mémoire et en dérivant des bornes de généralisation serrées
In 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
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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.

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Seifi, 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.

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This thesis proposes a novel approach to attribute clustering. It exploits the strength of semi-supervised learning to improve the quality of attribute clustering particularly when labeled data is limited. The significance of this work derives in part from the broad, and increasingly important, usage of attribute clustering to address outstanding problems within the machine learning community. This form of clustering has also been shown to have strong practical applications, being usable in heavyweight industrial applications. Although researchers have focused on supervised and unsupervised attribute clustering in recent years, semi-supervised attribute clustering has not received substantial attention. In this research, we propose an innovative two step iterative semi-supervised attribute clustering framework. This new framework, in each iteration, uses the result of attribute clustering to improve a classifier. It then uses the classifier to augment the training data used by attribute clustering in next iteration. This iterative framework outputs an improved classifier and attribute clustering at the same time. It gives more accurate clusters of attributes which better fit the real relations between attributes. In this study we proposed two new usages for attribute clustering to improve classification: solving the automatic view definition problem for multi-view learning and improving missing attribute-value handling at induction and prediction time. The application of these two new usages of attribute clustering in our proposed semi-supervised attribute clustering is evaluated using real world data sets from different domains.
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Li, 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.

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Soares, 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/.

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Atualmente, o meio mais comum de busca de informações é a WEB. Assim, é importante procurar métodos eficientes para recuperar essa informação. As máquinas de busca na WEB usualmente utilizam palavras-chaves para expressar uma busca. Porém, não é trivial caracterizar a informação desejada. Usuários diferentes com necessidades diferentes podem estar interessados em informações relacionadas, mas distintas, ao realizar a mesma busca. O processo de realimentação de relevância torna possível a participação ativa do usuário no processo de busca. A idéia geral desse processo consiste em, após o usuário realizar uma busca na WEB permitir que indique, dentre os sites encontrados, quais deles considera relevantes e não relevantes. A opinião do usuário pode então ser considerada para reordenar os dados, de forma que os sites relevantes para o usuário sejam retornados mais facilmente. Nesse contexto, e considerando que, na grande maioria dos casos, uma consulta retorna um número muito grande de sites WEB que a satisfazem, das quais o usuário é responsável por indicar um pequeno número de sites relevantes e não relevantes, tem-se o cenário ideal para utilizar aprendizado parcialmente supervisionado, pois essa classe de algoritmos de aprendizado requer um número pequeno de exemplos rotulados e um grande número de exemplos não-rotulados. Assim, partindo da hipótese que a utilização de aprendizado parcialmente supervisionado é apropriada para induzir um classificador que pode ser utilizado como um filtro de realimentação de relevância para buscas na WEB, o objetivo deste trabalho consiste em explorar algoritmos de aprendizado parcialmente supervisionado, mais especificamente, aqueles que utilizam multidescrição de dados, para auxiliar na recuperação de sites na WEB. Para avaliar esta hipótese foi projetada e desenvolvida uma ferramenta denominada C-SEARCH que realiza esta reordenação dos sites a partir da indicação do usuário. Experimentos mostram que, em casos que buscas genéricas, que o resultado possui um bom diferencial entre sites relevantes e irrelevantes, o sistema consegue obter melhores resultados para o usuário
As 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
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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.

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De nos jours, dans plusieurs domaines, tels que la bio-informatique ou le multimédia, les données peuvent être représentées par plusieurs ensembles d'attributs, appelés des vues. Pour une tâche de classification donnée, nous distinguons deux types de vues : les vues fortes sont celles adaptées à la tâche, les vues faibles sont adaptées à une (petite) partie de la tâche ; en classification multi-classes, chaque vue peut s'avérer forte pour reconnaître une classe, et faible pour reconnaître d’autres classes : une telle vue est dite déséquilibrée. Les travaux présentés dans cette thèse s'inscrivent dans le cadre de l'apprentissage supervisé et ont pour but de traiter les questions d'apprentissage multi-vue dans le cas des vues fortes, faibles et déséquilibrées. La première contribution de cette thèse est un algorithme d'apprentissage multi-vues théoriquement fondé sur le cadre de boosting multi-classes utilisé par AdaBoost.MM. La seconde partie de cette thèse concerne la mise en place d'un cadre général pour les méthodes d'apprentissage de classes déséquilibrées (certaines classes sont plus représentées que les autres). Dans la troisième partie, nous traitons le problème des vues déséquilibrées en combinant notre approche des classes déséquilibrées et la coopération entre les vues mise en place pour appréhender la classification multi-vues. Afin de tester les méthodes sur des données réelles, nous nous intéressons au problème de classification d'appels téléphoniques, qui a fait l'objet du projet ANR DECODA. Ainsi chaque partie traite différentes facettes du problème
Nowadays, 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
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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/.

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Em Aprendizado de Máquina, a abordagem supervisionada normalmente necessita de um número significativo de exemplos de treinamento para a indução de classificadores precisos. Entretanto, a rotulação de dados é freqüentemente realizada manualmente, o que torna esse processo demorado e caro. Por outro lado, exemplos não-rotulados são facilmente obtidos se comparados a exemplos rotulados. Isso é particularmente verdade para tarefas de classificação de textos que envolvem fontes de dados on-line tais como páginas de internet, email e artigos científicos. A classificação de textos tem grande importância dado o grande volume de textos disponível on-line. Aprendizado semi-supervisionado, uma área de pesquisa relativamente nova em Aprendizado de Máquina, representa a junção do aprendizado supervisionado e não-supervisionado, e tem o potencial de reduzir a necessidade de dados rotulados quando somente um pequeno conjunto de exemplos rotulados está disponível. Este trabalho descreve o algoritmo de aprendizado semi-supervisionado co-training, que necessita de duas descrições de cada exemplo. Deve ser observado que as duas descrições necessárias para co-training podem ser facilmente obtidas de documentos textuais por meio de pré-processamento. Neste trabalho, várias extensões do algoritmo co-training foram implementadas. Ainda mais, foi implementado um ambiente computacional para o pré-processamento de textos, denominado PreTexT, com o objetivo de utilizar co-training em problemas de classificação de textos. Os resultados experimentais foram obtidos utilizando três conjuntos de dados. Dois conjuntos de dados estão relacionados com classificação de textos e o outro com classificação de páginas de internet. Os resultados, que variam de excelentes a ruins, mostram que co-training, similarmente a outros algoritmos de aprendizado semi-supervisionado, é afetado de maneira bastante complexa pelos diferentes aspectos na indução dos modelos.
In 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.
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Twinanda, Andru Putra. "Vision-based approaches for surgical activity recognition using laparoscopic and RBGD videos." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD005/document.

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Cette thèse a pour objectif la conception de méthodes pour la reconnaissance automatique des activités chirurgicales. Cette reconnaissance est un élément clé pour le développement de systèmes réactifs au contexte clinique et pour des applications comme l’assistance automatique lors de chirurgies complexes. Nous abordons ce problème en utilisant des méthodes de Vision puisque l’utilisation de caméras permet de percevoir l’environnement sans perturber la chirurgie. Deux types de vidéos sont utilisées : des vidéos laparoscopiques et des vidéos multi-vues RGBD. Nous avons d’abord étudié les résultats obtenus avec les méthodes de l’état de l’art, puis nous avons proposé des nouvelles approches basées sur le « Deep learning ». Nous avons aussi généré de larges jeux de données constitués d’enregistrements de chirurgies. Les résultats montrent que nos méthodes permettent d’obtenir des meilleures performances pour la reconnaissance automatique d’activités chirurgicales que l’état de l’art
The 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
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Books on the topic "Multi-view machine learning"

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Fu, Yun, Zhengming Ding, and Handong Zhao. Learning Representation for Multi-View Data Analysis: Models and Applications. Springer, 2018.

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Book chapters on the topic "Multi-view machine learning"

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Brefeld, Ulf, Christoph Büscher, and Tobias Scheffer. "Multi-view Discriminative Sequential Learning." In Machine Learning: ECML 2005, 60–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_11.

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Muthu Lakshmi, G., and N. Krishnammal. "Multi-View Representation Learning." In Prediction and Analysis for Knowledge Representation and Machine Learning, 175–98. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898-9.

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Karami, Mahdi. "Deep Generative Multi-view Learning." In Machine Learning and Knowledge Discovery in Databases, 465–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_38.

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Xu, Xiangyu, Nuoya Xu, Huijie Li, and Qi Zhu. "Multi-spectral Palmprint Recognition with Deep Multi-view Representation Learning." In Machine Learning and Intelligent Communications, 748–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_61.

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Yu, Hong, Yahong Lian, Shu Li, and JiaXin Chen. "View-Weighted Multi-view K-means Clustering." In Artificial Neural Networks and Machine Learning – ICANN 2017, 305–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_35.

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Khan, Suleiman A., and Samuel Kaski. "Bayesian Multi-view Tensor Factorization." In Machine Learning and Knowledge Discovery in Databases, 656–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44848-9_42.

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Chen, Mickaël, and Ludovic Denoyer. "Multi-view Generative Adversarial Networks." In Machine Learning and Knowledge Discovery in Databases, 175–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_11.

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Zhu, Xiaofeng, Heung-Il Suk, Yonghua Zhu, Kim-Han Thung, Guorong Wu, and Dinggang Shen. "Multi-view Classification for Identification of Alzheimer’s Disease." In Machine Learning in Medical Imaging, 255–62. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_31.

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Yang, Longqi, Liangliang Zhang, and Yuhua Tang. "Online Binary Incomplete Multi-view Clustering." In Machine Learning and Knowledge Discovery in Databases, 75–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_5.

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Liu, Jian-wei, Xi-hao Ding, Run-kun Lu, Yuan-feng Lian, Dian-zhong Wang, and Xiong-lin Luo. "Multi-View Capsule Network." In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation, 152–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30487-4_13.

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Conference papers on the topic "Multi-view machine learning"

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Xijiong Xie and Shiliang Sun. "Multi-view clustering ensembles." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890443.

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Tao, Yingshan, Haoliang Yuan, Chun Sing Lai, and Loi Lei Lai. "Multi-View Collaborative Representation Classification." In 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. http://dx.doi.org/10.1109/icmlc48188.2019.8949323.

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Xu Yang, Xin Yang, and Hui-Lin Xiong. "Multi-view face detection with the multi-resolution MPP classifiers." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212334.

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Man, Hong, Shuanglu Dai, Victor Lawrence, Thomas A. LaPeruta, and Myron E. Hohil. "Unsupervised multi-view object proposal ranking." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2587810.

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Yang, Yitao, Xiucai Ye, and Tetsuya Sakurai. "Multi-View Federated Learning with Data Collaboration." In ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3529836.3529904.

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Wiles, Olivia, and Andrew Zisserman. "SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.99.

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Kwong, J. N. S., and S. Gong. "Learning Support Vector Machines for A Multi-View Face Model." In British Machine Vision Conference 1999. British Machine Vision Association, 1999. http://dx.doi.org/10.5244/c.13.50.

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Srisawat, Chutiphon, and Janjira Payakpate. "Multi-Criteria Decision Making - developer view: Applications in Thailand." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890865.

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Yan, Jie. "Ensemble SVM Regression Based Multi-View Face Detection System." In 2007 IEEE Workshop on Machine Learning for Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/mlsp.2007.4414300.

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Wang, Xing-qi. "Research on Multi-View Semi-Supervised Learning Algorithm Based on Co-Learning." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258652.

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