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Статті в журналах з теми "Multi-view machine learning"
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.
Повний текст джерелаWang, 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.
Повний текст джерелаSun, 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.
Повний текст джерелаKaraaba, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаTang, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаWan, 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.
Повний текст джерела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.
Повний текст джерелаLi, 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.
Повний текст джерелаДисертації з теми "Multi-view machine learning"
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
Книги з теми "Multi-view machine learning"
Fu, Yun, Zhengming Ding, and Handong Zhao. Learning Representation for Multi-View Data Analysis: Models and Applications. Springer, 2018.
Знайти повний текст джерелаЧастини книг з теми "Multi-view machine learning"
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.
Повний текст джерелаMuthu 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.
Повний текст джерелаKarami, 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.
Повний текст джерелаXu, 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.
Повний текст джерелаYu, 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.
Повний текст джерелаKhan, 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.
Повний текст джерелаChen, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаТези доповідей конференцій з теми "Multi-view machine learning"
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.
Повний текст джерелаTao, 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.
Повний текст джерелаXu 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.
Повний текст джерелаMan, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаWiles, 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.
Повний текст джерелаKwong, 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.
Повний текст джерелаSrisawat, 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.
Повний текст джерелаYan, 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.
Повний текст джерелаWang, 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.
Повний текст джерела