Tesis sobre el tema "Limited training data"
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Chang, Eric I.-Chao. "Improving wordspotting performance with limited training data". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38056.
Texto completoIncludes bibliographical references (leaves 149-155).
by Eric I-Chao Chang.
Ph.D.
Zama, Ramirez Pierluigi <1992>. "Deep Scene Understanding with Limited Training Data". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9815/1/zamaramirez_pierluigi_tesi.pdf.
Texto completoMcLaughlin, N. R. "Robust multimodal person identification given limited training data". Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579747.
Texto completoLi, Jiawei. "Person re-identification with limited labeled training data". HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/541.
Texto completoQu, Lizhen [Verfasser] y Gerhard [Akademischer Betreuer] Weikum. "Sentiment analysis with limited training data / Lizhen Qu. Betreuer: Gerhard Weikum". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2013. http://d-nb.info/1053680104/34.
Texto completoGuo, Zhenyu. "Data famine in big data era : machine learning algorithms for visual object recognition with limited training data". Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.
Texto completoSäfdal, Joakim. "Data-Driven Engine Fault Classification and Severity Estimation Using Interpolated Fault Modes from Limited Training Data". Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173916.
Texto completoLapin, Maksim [Verfasser] y Bernt [Akademischer Betreuer] Schiele. "Image classification with limited training data and class ambiguity / Maksim Lapin ; Betreuer: Bernt Schiele". Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2017. http://d-nb.info/1136607927/34.
Texto completoTrávníčková, Kateřina. "Interaktivní segmentace 3D CT dat s využitím hlubokého učení". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432864.
Texto completoMorgan, Joseph Troy. "Adaptive hierarchical classification with limited training data". Thesis, 2002. http://wwwlib.umi.com/cr/utexas/fullcit?p3115506.
Texto completoHuang, Jing-Ting y 黃敬庭. "Towards Adversarial Training for Data-limited TopicClassification". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/za79tm.
Texto completo國立臺灣大學
資訊網路與多媒體研究所
107
The quantity of the data today already surpassed the amount a man can handle. How to deal with and filter enormous text data with the help of machine is becoming more and more important. However, classification task on text data requires enough labeled data to back up the training procedure. If we somehow want to deal with this issue, it’s surely a problem we have to deal with. Before starting on this work, we observed multiple news example from Taiwanese online media and found some pattern that we can take advantage of: first, we found that some news about similar topics tend to appear over and over again. We assumed that news of these topics might be more tempting and thus can attract more readers and hype up emotions. This is a good place to start the work since news of these topics might appear again in the near future thus our work will have immediate impact. Second, we found that even if news of these topics tends to appear again and again, the objective and minor details are usually completely different. Using traditional word matching model on this task might not work very well. Based on above reasons, we propose an approach to use the few seed sentences we get from past news on these events to generate training data. Furthermore, for events of similar topic, we define a higher-level topic to include these events. We generate positive and negative training example based on seed sentences and propose a model that can take fully advantage of our generated dataset on classification task. A series of experiments are also conducted to measure the capability of our approach. To realize our approach, we crawled news articles published by public news media during the past 5 to 10 years to build a corpus from which we can sample negative data. Then for each higher-level topic we generate positive and negative datasets. In this work, our main approach can be divided into two part. The first part being the retrieval and generation of dataset and the second part being the training of classifier using the data generated. Our experiment results showed that generation and augmentation we applied can help boosting the performance on this task.
Pun, Iek-Kuong y 潘亦廣. "Hierarchical-searching-based hand tracking with limited training data". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/18887351093169054772.
Texto completo國立交通大學
資訊科學與工程研究所
100
In this thesis, we consider tracking an articulated hand without using markers. Our hand tracking method performs nearest-neighbor-based search in a 3D hand model large database. For robustly and efficiently, we choose to capture a small real hand images database for each user as an intermediate dataset. And use Hierarchical-searching and temporal consistency to efficiently search in the large database and disambiguate the result. Our prototype system can estimate hand pose including rigid and non-rigid out-of-image-plane rotation, slow and fast gesture charging when rotation, and recover after the hand left the camera in real time. We believe it can be a more intuitive way for advance human computer interaction.
"An Effective Approach to Biomedical Information Extraction with Limited Training Data". Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.8903.
Texto completoDissertation/Thesis
Ph.D. Biomedical Informatics 2011
Wikén, Victor. "An Investigation of Low-Rank Decomposition for Increasing Inference Speed in Deep Neural Networks With Limited Training Data". Thesis, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235370.
Texto completoFör att öka inferenshastigheten hos faltningssnätverk, har i denna studie optimeringstekniken low-rank tensor decomposition implementerats och applicerats på AlexNet, som har tränats för att klassificera hundraser. På grund av en begränsad mängd träningsdata användes transfer learning för uppgiften. Syftet med studien är att undersöka hur effektiv low-rank tensor decomposition är när träningsdatan är begränsad. Jämfört med resultaten från en tidigare studie visar resultaten från denna studie att det finns ett starkt samband mellan effekterna av low-rank tensor decomposition och hur mycket tillgänglig träningsdata som finns. En signifikant hastighetsökning kan uppnås i de olika faltningslagren med hjälp av low-rank tensor decomposition. Eftersom det finns ett behov av att träna om nätverket efter dekompositionen och på grund av den begränsade mängden data så uppnås hastighetsökningen dock på bekostnad av en viss minskning i precisionen för modellen.
Nayak, Gaurav Kumar. "Data-efficient Deep Learning Algorithms for Computer Vision Applications". Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6094.
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