Готові списки джерел за темами / Small datasets

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Автор: Grafiati

Опубліковано: 8 червня 2024

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Статті в журналах з теми "Small datasets":

Agliari, Elena, Francesco Alemanno, Miriam Aquaro, Adriano Barra, Fabrizio Durante, and Ido Kanter. "Hebbian dreaming for small datasets." Neural Networks 173 (May 2024): 106174. http://dx.doi.org/10.1016/j.neunet.2024.106174.

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Ingrassia, Salvatore, and Isabella Morlini. "Neural Network Modeling for Small Datasets." Technometrics 47, no. 3 (August 2005): 297–311. http://dx.doi.org/10.1198/004017005000000058.

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Ricchiuto, Piero, Judy C. G. Sng, and Wilson Wen Bin Goh. "Analysing extremely small sized ratio datasets." International Journal of Bioinformatics Research and Applications 11, no. 3 (2015): 268. http://dx.doi.org/10.1504/ijbra.2015.069225.

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Tuomo, Alasalmi, Jaakko Suutala, Juha Röning, and Heli Koskimäki. "Better Classifier Calibration for Small Datasets." ACM Transactions on Knowledge Discovery from Data 14, no. 3 (May 14, 2020): 1–19. http://dx.doi.org/10.1145/3385656.

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Montalvão, J., R. Attux, and D. G. Silva. "Simple entropy estimator for small datasets." Electronics Letters 48, no. 17 (August 16, 2012): 1059–61. http://dx.doi.org/10.1049/el.2012.2002.

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Khobragade, Vandana, M. S. Pradeep Kumar Patnaik, and Srinivasa Rao Sura. "Revaluating Pretraining in Small Size Training Sample Regime." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 694–704. http://dx.doi.org/10.37391/ijeer.100346.

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Deep neural network (DNN) based models are highly acclaimed in medical image classification. The existing DNN architectures are claimed to be at the forefront of image classification. These models require very large datasets to classify the images with a high level of accuracy. However, fail to perform when trained on datasets of small size. Low accuracy and overfitting are the problems observed when medical datasets of small sizes are used to train a classifier using deep learning models such as Convolutional Neural Networks (CNN). These existing methods and models either always overfit when training on these small datasets or will result in classification accuracy which tends towards randomness. This issue stands even when using Transfer Learning (TL), the current standard for such a scenario. In this paper, we have tested several models including ResNet and VGGs along with more modern models like MobileNets on different medical datasets with transfer learning and without transfer learning. We have proposed solid theories as to why there exists a need for a more novel approach to this issue, and how the current methodologies fail when applied to the aforementioned datasets. Larger, more complex models are not able to converge for smaller datasets. Smaller models with less complexity perform better on the same dataset than their larger model counterparts.

Burmakova, Anastasiya, and Diana Kalibatienė. "Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment." Applied Sciences 12, no. 16 (August 18, 2022): 8252. http://dx.doi.org/10.3390/app12168252.

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Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques and FIS for the prediction of the consequences of oil spills on ground environments using small datasets. Consequently, we present a hybrid approach for assessing the suitability of ML (Linear Regression, Decision Trees, Support Vector Regression, Ensembles, and Gaussian Process Regression) and the adaptive neural fuzzy inference system (ANFIS) for predicting the consequences of oil spills with a small dataset. This paper proposes enlarging the initial small dataset of an oil spill on a ground environment by using the synthetic data generated by applying a mathematical model. ML techniques and ANFIS were tested with the same generated synthetic datasets to assess the proposed approach. The proposed ANFIS-based approach shows significant performance and sufficient efficiency for predicting the consequences of oil spills on ground environments with a smaller dataset than the applied ML techniques. The main finding of this paper indicates that FIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results.

Jamjoom, Mona. "The pertinent single-attribute-based classifier for small datasets classification." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3227. http://dx.doi.org/10.11591/ijece.v10i3.pp3227-3234.

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Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attribute-based-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).

Petráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni, and Jozef Dudiak. "Benford’s Law in Electric Distribution Network." Mathematics 11, no. 18 (September 10, 2023): 3863. http://dx.doi.org/10.3390/math11183863.

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Benford’s law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford’s law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford’s law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford’s law can be applied and that it can extract the dataset’s artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention.

Andonie, Răzvan. "Extreme Data Mining: Inference from Small Datasets." International Journal of Computers Communications & Control 5, no. 3 (September 1, 2010): 280. http://dx.doi.org/10.15837/ijccc.2010.3.2481.

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<p>Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets.<br /> We have the following goals: i. To discuss the meaning of "small" in the context of inferring from small datasets. ii. To overview computational intelligence solutions for this problem. iii. To illustrate the introduced concepts with a real-life application.</p>

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Дисертації з теми "Small datasets":

Shi, Xiaojin. "Visual learning from small training datasets /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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Van, Koten Chikako, and n/a. "Bayesian statistical models for predicting software effort using small datasets." University of Otago. Department of Information Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20071009.120134.

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The need of today�s society for new technology has resulted in the development of a growing number of software systems. Developing a software system is a complex endeavour that requires a large amount of time. This amount of time is referred to as software development effort. Software development effort is the sum of hours spent by all individuals involved. Therefore, it is not equal to the duration of the development. Accurate prediction of the effort at an early stage of development is an important factor in the successful completion of a software system, since it enables the developing organization to allocate and manage their resource effectively. However, for many software systems, accurately predicting the effort is a challenge. Hence, a model that assists in the prediction is of active interest to software practitioners and researchers alike. Software development effort varies depending on many variables that are specific to the system, its developmental environment and the organization in which it is being developed. An accurate model for predicting software development effort can often be built specifically for the target system and its developmental environment. A local dataset of similar systems to the target system, developed in a similar environment, is then used to calibrate the model. However, such a dataset often consists of fewer than 10 software systems, causing a serious problem in the prediction, since predictive accuracy of existing models deteriorates as the size of the dataset decreases. This research addressed this problem with a new approach using Bayesian statistics. This particular approach was chosen, since the predictive accuracy of a Bayesian statistical model is not so dependent on a large dataset as other models. As the size of the dataset decreases to fewer than 10 software systems, the accuracy deterioration of the model is expected to be less than that of existing models. The Bayesian statistical model can also provide additional information useful for predicting software development effort, because it is also capable of selecting important variables from multiple candidates. In addition, it is parametric and produces an uncertainty estimate. This research developed new Bayesian statistical models for predicting software development effort. Their predictive accuracy was then evaluated in four case studies using different datasets, and compared with other models applicable to the same small dataset. The results have confirmed that the best new models are not only accurate but also consistently more accurate than their regression counterpart, when calibrated with fewer than 10 systems. They can thus replace the regression model when using small datasets. Furthermore, one case study has shown that the best new models are more accurate than a simple model that predicts the effort by calculating the average value of the calibration data. Two case studies has also indicated that the best new models can be more accurate for some software systems than a case-based reasoning model. Since the case studies provided sufficient empirical evidence that the new models are generally more accurate than existing models compared, in the case of small datasets, this research has produced a methodology for predicting software development effort using the new models.

Zhao, Amy(Xiaoyu Amy). "Learning distributions of transformations from small datasets for applied image synthesis." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/128342.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2020
Cataloged from PDF of thesis. "February 2020."
Includes bibliographical references (pages 75-91).
Much of the recent research in machine learning and computer vision focuses on applications with large labeled datasets. However, in realistic settings, it is much more common to work with limited data. In this thesis, we investigate two applications of image synthesis using small datasets. First, we demonstrate how to use image synthesis to perform data augmentation, enabling the use of supervised learning methods with limited labeled data. Data augmentation -- typically the application of simple, hand-designed transformations such as rotation and scaling -- is often used to expand small datasets. We present a method for learning complex data augmentation transformations, producing examples that are more diverse, realistic, and useful for training supervised systems than hand-engineered augmentation. We demonstrate our proposed augmentation method for improving few-shot object classification performance, using a new dataset of collectible cards with fine-grained differences. We also apply our method to medical image segmentation, enabling the training of a supervised segmentation system using just a single labeled example. In our second application, we present a novel image synthesis task: synthesizing time lapse videos of the creation of digital and watercolor paintings. Using a recurrent model of paint strokes and a novel training scheme, we create videos that tell a plausible visual story of the painting process.
by Amy (Xiaoyu) Zhao.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

Arzamasov, Vadim [Verfasser], and K. [Akademischer Betreuer] Böhm. "Comprehensible and Robust Knowledge Discovery from Small Datasets / Vadim Arzamasov ; Betreuer: K. Böhm." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1238148166/34.

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Lazarovici, Allan 1979. "Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29681.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaves 60-62).
A computer program called FUNSCAN was developed which identifies protein coding regions in fungal genomes. Gene structural and compositional properties are modeled using a Hidden Markov Model. Separate training and testing sets for FUNSCAN were obtained by aligning cDNAs from an organism to their genomic loci, generating a 'gold standard' set of annotated genes. The performance of FUNSCAN is competitive with other computer programs design to identify protein coding regions in fungal genomes. A technique called 'Training Set Augmentation' is described which can be used to train FUNSCAN when only a small training set of genes is available. Techniques that combine alignment algorithms with FUNSCAN to identify novel genes are also discussed and explored.
by Allan Lazarovici.
M.Eng.and S.B.

Horečný, Peter. "Metody segmentace obrazu s malými trénovacími množinami." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412996.

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The goal of this thesis was to propose an image segmentation method, which is capable of effective segmentation process with small datasets. Recently published ODE neural network was used for this method, because its features should provide better generalization in case of tasks with only small datasets available. The proposed ODE-UNet network was created by combining UNet architecture with ODE neural network, while using benefits of both networks. ODE-UNet reached following results on ISBI dataset: Rand: 0,950272 and Info: 0,978061. These results are better than the ones received from UNet model, which was also tested in this thesis, but it has been proven that state of the art can not be outperformed using ODE neural networks. However, the advantages of ODE neural network over tested UNet architecture and other methods were confirmed, and there is still a room for improvement by extending this method.

Lucy, Caleb O. "Rapid Acquisition of Low Cost High-Resolution Elevation Datasets Using a Small Unmanned Aircraft System: An Application for Measuring River Geomorphic Change." Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104880.

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Thesis advisor: Noah P. Snyder
Emerging methods for acquiring high-resolution topographic datasets have the potential to open new opportunities for quantitative geomorphic analysis. This study demonstrates a technique for rapidly obtaining structure from motion (SfM) photogrammetry-derived digital elevation models (DEMs) using aerial photographs acquired with a small unmanned aircraft system (sUAS). In conjunction with collection of aerial imagery, study sites are surveyed with a differential global position system (dGPS)-enabled total station (TPS) for georeferencing and accuracy assessment of sUAS SfM measurements. Results from sUAS SfM surveys of upland river channels in northern New England consistently produce DEMs and orthoimagery with ~1 cm pixel resolution. One-to-one point measurement comparisons demonstrate sUAS SfM systematically measures elevations about 0.16 ±0.23 m higher than TPS equivalents (0.28 m RMSE). Bathymetric (i.e. submerged or subaqueous) sUAS SfM measurements are 0.20 ±0.24 m (0.31 m RMSE) higher than TPS, whereas exposed (subaerial) points are 0.14 ±0.22 m (0.26 m RMSE) higher than TPS. Serial comparison of DEMs obtained before and after a two-year flood event indicates cut bank erosion and point bar deposition of ~0.10 m, consistent with expectations for channel evolution. DEMs acquired with the sUAS SfM are of comparable resolution but a lower cost alternative to those from airborne light detection and ranging (lidar), the current standard for topographic imagery. Furthermore, lidar is not available for much of the United States and sUAS SfM provides an efficient means for expanding coverage of this critical elevation dataset. Due to their utility in municipal, land use, and emergency planning, the demand for high-resolution topographic datasets continues to increase among governments, research institutions, and private sector consulting firms. Terrain analysis using sUAS SfM could therefore be a boon to river management and restoration in northern New England and other regions
Thesis (MS) — Boston College, 2015
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Geology and Geophysics

Oppon, Ekow CruickShank. "Synergistic use of promoter prediction algorithms: a choice of small training dataset?" Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.

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Promoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo
wet&rsquo
laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.

Forsberg, Fredrik, and Gonzalez Pierre Alvarez. "Unsupervised Machine Learning: An Investigation of Clustering Algorithms on a Small Dataset." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16300.

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Context: With the rising popularity of machine learning, looking at its shortcomings is valuable in seeing how well machine learning is applicable. Is it possible to apply the clustering with a small dataset? Objectives: This thesis consists of a literature study, a survey and an experiment. It investigates how two different unsupervised machine learning algorithms DBSCAN(Density-Based Spatial Clustering of Applications with Noise) and K-means run on a dataset gathered from a survey. Methods: Making a survey where we can see statistically what most people chose and apply clustering with the data from the survey to confirm if the clustering has the same patterns as what people have picked statistically. Results: It was possible to identify patterns with clustering algorithms using a small dataset. The literature studies show examples that both algorithms have been used successfully. Conclusions: It's possible to see patterns using DBSCAN and K-means on a small dataset. The size of the dataset is not necessarily the only aspect to take into consideration, feature and parameter selection are both important as well since the algorithms need to be tuned and customized to the data.

Gay, Antonin. "Pronostic de défaillance basé sur les données pour la prise de décision en maintenance : Exploitation du principe d'augmentation de données avec intégration de connaissances à priori pour faire face aux problématiques du small data set." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0059.

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Cette thèse CIFRE est un projet commun entre ArcelorMittal et le laboratoire CRAN, dont l'objectif est d'optimiser la prise de décision en maintenance industrielle par l'exploitation des sources d'information disponibles, c'est-à-dire des données et des connaissances industrielles, dans le cadre des contraintes industrielles présentées par le contexte sidérurgique. La stratégie actuelle de maintenance des lignes sidérurgiques est basée sur une maintenance préventive régulière. L'évolution de la maintenance préventive vers une stratégie dynamique se fait par le biais de la maintenance prédictive. La maintenance prédictive a été formalisée au sein du paradigme Prognostics and Health Management (PHM) sous la forme d'un processus en sept étapes. Parmi ces étapes de la PHM, le travail de ce doctorat se concentre sur la prise de décision et le pronostic. En regard de cette maintenance prédictive, le contexte de l'Industrie 4.0 met l'accent sur les approches basées sur les données, qui nécessitent une grande quantité de données que les systèmes industriels ne peuvent pas fournir systématiquement. La première contribution de la thèse consiste donc à proposer une équation permettant de lier les performances du pronostic au nombre d'échantillons d'entraînement disponibles. Cette contribution permet de prédire quelles performances le pronostic pourraient atteindre avec des données supplémentaires dans le cas de petits jeux de données (small datasets). La deuxième contribution de la thèse porte sur l'évaluation et l'analyse des performances de l'augmentation de données appliquée au pronostic sur des petits jeux de données. L'augmentation de données conduit à une amélioration de la performance du pronostic jusqu'à 10%. La troisième contribution de la thèse est l'intégration de connaissances expertes au sein de l'augmentation de données. L'intégration de connaissances statistiques s'avère efficace pour éviter la dégradation des performances causée par l'augmentation de données sous certaines conditions défavorables. Enfin, la quatrième contribution consiste en l'intégration des résultats du pronostic dans la modélisation des coûts de la prise de décision en maintenance et en l'évaluation de l'impact du pronostic sur ce coût. Elle démontre que (i) la mise en œuvre de la maintenance prédictive réduit les coûts de maintenance jusqu'à 18-20% et (ii) l'amélioration de 10% du pronostic peut réduire les coûts de maintenance de 1% supplémentaire
This CIFRE PhD is a joint project between ArcelorMittal and the CRAN laboratory, with theaim to optimize industrial maintenance decision-making through the exploitation of the available sources of information, i.e. industrial data and knowledge, under the industrial constraints presented by the steel-making context. Current maintenance strategy on steel lines is based on regular preventive maintenance. Evolution of preventive maintenance towards a dynamic strategy is done through predictive maintenance. Predictive maintenance has been formalized within the Prognostics and Health Management (PHM) paradigm as a seven steps process. Among these PHM steps, this PhD's work focuses on decision-making and prognostics. The Industry 4.0 context put emphasis on data-driven approaches, which require large amount of data that industrial systems cannot ystematically supply. The first contribution of the PhD consists in proposing an equation to link prognostics performances to the number of available training samples. This contribution allows to predict prognostics performances that could be obtained with additional data when dealing with small datasets. The second contribution of the PhD focuses on evaluating and analyzing the performance of data augmentation when applied to rognostics on small datasets. Data augmentation leads to an improvement of prognostics performance up to 10%. The third contribution of the PhD consists in the integration of expert knowledge into data augmentation. Statistical knowledge integration proved efficient to avoid performance degradation caused by data augmentation under some unfavorable conditions. Finally, the fourth contribution consists in the integration of prognostics in maintenance decision-making cost modeling and the evaluation of prognostics impact on maintenance decision cost. It demonstrates that (i) the implementation of predictive maintenance reduces maintenance cost up to 18-20% and ii) the 10% prognostics improvement can reduce maintenance cost by an additional 1%

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Книги з теми "Small datasets":

Sarang, Poornachandra. Clustering Small to Humongous Datasets. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49094-1.

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Machine Learning Methods with Noisy, Incomplete or Small Datasets. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1288-4.

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Schoot, Rens van de, and Milica Miočević. Small Sample Size Solutions. Taylor & Francis Group, 2020.

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Schoot, Rens van de, and Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.

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Schoot, Rens van de, and Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.

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Schoot, Rens van de, and Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.

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Schoot, Rens van de, and Milica Miočević. Small Sample Size Solutions: A How to Guide for Applied Researchers and Practitioners. Taylor & Francis Group, 2020.

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Woldu, Gabriel Temesgen. Do fiscal regimes matter for fiscal sustainability in South Africa? A Markov-switching approach. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/920-4.

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This paper empirically examines South Africa’s fiscal sustainability through a Markov-switching model which utilizes quarterly datasets for the period from 1960 to 2019. The results show that public debt responds positively, demonstrating a sustainable fiscal policy. Furthermore, considering the regime-specific feedback coefficients of the fiscal policy rule and the durations of fiscal regimes, the study finds that South Africa’s fiscal policy satisfies the No-Ponzi game condition. Therefore, from a policy perspective, the South African government should take measures such as pension reforms, reducing operational expenses, reducing subsidies, and funding micro and small enterprises to gain the double dividend on the expenditure side along with revenue-enhancing measures on consumption taxes to achieve stable public finances and lower debt levels.

Tyrkkö, Jukka. Discovering the Past for Yourself. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190611040.003.0012.

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This chapter outlines the state of the art in corpus-based language teaching and digital pedagogy, focusing on the differences between using corpora with present-day and historical data. The basic concepts of corpus-based research such as representativeness, frequency, and statistical significance can be introduced to students who are new to corpus methods, and the application of these concepts to the history of English can deepen students’ understanding of how historical varieties of the language are researched. This chapter will also address some of the key challenges particular to teaching the history of English using corpora, such as dealing with the seemingly counterintuitive findings, non-standard features, and small datasets. Finally, following an overview of available historical corpora and corpus tools, several practical examples of corpus-driven activities will be discussed in detail, with suggestions and ideas on how a teacher might prepare and run corpus-based lessons.

Lewis, Oliver. Council of Europe. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786627.003.0004.

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This chapter presents an overview of the adjudicative bodies of the Council of Europe—namely, the European Court of Human Rights (established by the European Convention on Human Rights and Fundamental Freedoms (ECHR)) and the European Committee of Social Rights—and outlines their mandates with regard to integrating UN human rights treaties. It analyses how these two bodies have cited the Convention on the Rights of Persons with Disabilities (CRPD). The dataset was forty-five cases dealt with by the Court and two collective complaints decided by the Committee that cite the CRPD up to 2016. Notwithstanding the relatively small size of the dataset, the conclusions are that the Council of Europe system has yet to engage seriously in the CRPD’s jurisprudential opportunities. The reasons for this cannot be ascertained from a desk-based methodology, and further research is required.

Частини книг з теми "Small datasets":

Tulp, Jan Willem. "Designing for Small and Large Datasets." In New Challenges for Data Design, 377–90. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6596-5_20.

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Cadenas, José M., M. Carmen Garrido, and Raquel Martínez. "Fuzzy Discretization Process from Small Datasets." In Studies in Computational Intelligence, 263–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23392-5_15.

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Ibrikci, Turgay, Esra Mahsereci Karabulut, and Jean Dieu Uwisengeyimana. "Meta Learning on Small Biomedical Datasets." In Lecture Notes in Electrical Engineering, 933–39. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0557-2_89.

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Raul, Nataasha, Royston D’mello, and Mandar Bhalerao. "Keystroke Dynamics Authentication Using Small Datasets." In Communications in Computer and Information Science, 89–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7561-3_7.

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Dash, Amanda, and Alexandra Branzan Albu. "Texture-Based Data Augmentation for Small Datasets." In Advanced Concepts for Intelligent Vision Systems, 345–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45382-3_29.

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Raj, Akhilesh, Kanishk Gandhi, Bhanu Teja Nalla, and Nishchal K. Verma. "Object Detection and Recognition Using Small Labeled Datasets." In Advances in Intelligent Systems and Computing, 407–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1135-2_31.

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Bashar, Md Abul, Richi Nayak, Nicolas Suzor, and Bridget Weir. "Misogynistic Tweet Detection: Modelling CNN with Small Datasets." In Communications in Computer and Information Science, 3–16. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1_1.

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Racca, Alberto, and Luca Magri. "Statistical Prediction of Extreme Events from Small Datasets." In Computational Science – ICCS 2022, 707–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08757-8_58.

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Tato, Ange, and Roger Nkambou. "Deep Knowledge Tracing on Skills with Small Datasets." In Intelligent Tutoring Systems, 123–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09680-8_12.

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Ruiz, Victoria, Ángel Sánchez, José F. Vélez, and Bogdan Raducanu. "Waste Classification with Small Datasets and Limited Resources." In Intelligent Systems Reference Library, 185–203. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06307-7_10.

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Тези доповідей конференцій з теми "Small datasets":

Hiruta, Komei, Ryusuke Saito, Taro Hatakeyama, Atsushi Hashimoto, and Satoshi Kurihara. "Conditional GAN for Small Datasets." In 2022 IEEE International Symposium on Multimedia (ISM). IEEE, 2022. http://dx.doi.org/10.1109/ism55400.2022.00062.

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Steedman, Mark, Miles Osborne, Anoop Sarkar, Stephen Clark, Rebecca Hwa, Julia Hockenmaier, Paul Ruhlen, Steven Baker, and Jeremiah Crim. "Bootstrapping statistical parsers from small datasets." In the tenth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1067807.1067851.

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Xu, Peng, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, and Yanshuai Cao. "Optimizing Deeper Transformers on Small Datasets." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.163.

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Ndipenoch, Nchongmaje, Alina Miron, Zidong Wang, and Yongmin Li. "Retinal Image Segmentation with Small Datasets." In 10th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011779200003414.

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Wang, Jingjie, Xiang Wei, Siyang Lu, Mingquan Wang, Xiaoyu Liu, and Wei Lu. "Redesign Visual Transformer For Small Datasets." In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). IEEE, 2022. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00077.

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Gao, Haoqi, and Koichi Ogawara. "Face alignment by learning from small real datasets and large synthetic datasets." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00073.

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Xiao, Yabo, Shuai Guo, Tianqi Lv, and Lei Jin. "Target Detection on Small Sample Specific Datasets." In 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2018. http://dx.doi.org/10.1109/imccc.2018.00332.

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Yin, Mingjun, Zhiyong Chang, and Yan Wang. "Adaptive Hybrid Vision Transformer for Small Datasets." In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00132.

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Liu, Yanzhu, Adams Wai Kin Kong, and Chi Keong Goh. "Deep Ordinal Regression Based on Data Relationship for Small Datasets." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/330.

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Ordinal regression aims to classify instances into ordinal categories. As with other supervised learning problems, learning an effective deep ordinal model from a small dataset is challenging. This paper proposes a new approach which transforms the ordinal regression problem to binary classification problems and uses triplets with instances from different categories to train deep neural networks such that high-level features describing their ordinal relationship can be extracted automatically. In the testing phase, triplets are formed by a testing instance and other instances with known ranks. A decoder is designed to estimate the rank of the testing instance based on the outputs of the network. Because of the data argumentation by permutation, deep learning can work for ordinal regression even on small datasets. Experimental results on the historical color image benchmark and MSRA image search datasets demonstrate that the proposed algorithm outperforms the traditional deep learning approach and is comparable with other state-of-the-art methods, which are highly based on prior knowledge to design effective features.

Ashrafi, Parivash, Yi Sun, Neil Davey, Rod Adams, Marc B. Brown, Maria Prapopoulou, and Gary Moss. "The importance of hyperparameters selection within small datasets." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280645.

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Звіти організацій з теми "Small datasets":

Fraley, Chris, Adrian Raftery, and Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459790.

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Chahal, Husanjot, Helen Toner, and Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, September 2021. http://dx.doi.org/10.51593/20200075.

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Conventional wisdom suggests that cutting-edge artificial intelligence is dependent on large volumes of data. An overemphasis on “big data” ignores the existence—and underestimates the potential—of several AI approaches that do not require massive labeled datasets. This issue brief is a primer on “small data” approaches to AI. It presents exploratory findings on the current and projected progress in scientific research across these approaches, which country leads, and the major sources of funding for this research.

Kurmann, André, Étienne Lalé, and Lien Ta. Measuring Small Business Dynamics and Employment with Private-Sector Real-Time Data. CIRANO, August 2022. http://dx.doi.org/10.54932/xsph3669.

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The COVID-19 pandemic has led to an explosion of research using private-sector datasets to measure business dynamics and employment in real-time. Yet questions remain about the representativeness of these datasets and how to distinguish business openings and closings from sample churn – i.e., sample entry of already operating businesses and sample exits of businesses that continue operating. This paper proposes new methods to address these issues and applies them to the case of Homebase, a real-time dataset of mostly small service-sector sector businesses that has been used extensively in the literature to study the effects of the pandemic. We match the Homebase establishment records with information on business activity from Safegraph, Google, and Facebook to assess the representativeness of the data and to estimate the probability of business closings and openings among sample exits and entries. We then exploit the high frequency / geographic detail of the data to study whether small service-sector businesses have been hit harder by the pandemic than larger firms, and the extent to which the Paycheck Protection Program (PPP) helped small businesses keep their workforce employed. We find that our real-time estimates of small business dynamics and employment during the pandemic are remarkably representative and closely fit population counterparts from administrative data that have recently become available. Distinguishing business closings and openings from sample churn is critical for these results. We also find that while employment by small businesses contracted more severely in the beginning of the pandemic than employment of larger businesses, it also recovered more strongly thereafter. In turn, our estimates suggests that the rapid rollout of PPP loans significantly mitigated the negative employment effects of the pandemic. Business closings and openings are a key driver for both results, thus underlining the importance of properly correcting for sample churn.

Salter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker, and W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques and larger-scale transport properties. This study uses pre-existing medical CT-scan datasets of reference material, namely glass beads and conventional reservoir rocks (Berea sandstone, Boise sandstone, Indiana limestone) to extract the 3D geometry of connected pores using an open-source software (Spam). Pore networks from rock samples were generated from dry and then saturated samples. Binarized datasets were produced for these materials (generated by a thresholding technique) to obtain pore size distribution and tortuosity, as well as preferential paths for fluid flow. Average porosities were also calculated for comparison with those obtained by conventional commercial laboratory techniques. The results obtained show that this approach works well for medium and coarse-grained materials that do not contain a large percentage of fine particles. However, this approach does not allow representative networks to be obtained for fine-grained rocks, due to the fact that small pores (or pore throats) cannot be taken into account in the datasets obtained from the medical CT-Scan. A next step, using datasets produced from a micro- CT scan, is planned in order to be able to generate representative networks in this type of material as well.

Lers, Amnon, and Pamela J. Green. Analysis of Small RNAs Associated with Plant Senescence. United States Department of Agriculture, March 2013. http://dx.doi.org/10.32747/2013.7593393.bard.

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Senescence is an agriculturally significant process due to its negative impact to crop yield and postharvest quality. The genetic regulatory systems controlling senescence induction and progress respond to both developmental and environmental stress signals and involve numerous gene expression changes. Knowledge about the key molecular factors which control senescence is very limited. MicroRNAs (miRNAs) are a class of small RNAs which typically function by guiding cleavage of target messenger RNAs. They have been shown to play major roles in a variety of plant processes including development, responses to environmental stresses, and senescence. The long-term goal of this work is to elucidate roles of small RNAs associated with plant senescence. The hypothesis underlying this research is that miRNA-mediated regulation makes important contributions to the senescence process in plants. Specific, original research objectives included: 1) Profiling of small RNAs from senescing plants; 2) Data Analysis and public access via a user-friendly web interface; 3) Validation of senescence-associated miRNAs and target RNAs; 4) Development of transgenic plants for functional analysis of miRNAs in Arabidopsis. Major revisions made in the research compared to the original work plan included 1) Exclusion of the planned work with tomato as recommended by the BARD review panel; 2) Performing miRNA study also in senescing Arabidopsis siliques, in addition to senescing leaves. To identify senescenceregulation of miRNAs in Arabidopsis thaliana, eight small RNA libraries were constructed and sequenced at four different stages of development and senescence from both leaves and siliques, resulting in more than 200 million genome-matched sequences. Parallel Analysis of RNA Ends (PARE) libraries, which enable the large-scale examination of miRNA-guided cleavage products, were also constructed and sequenced, resulting in over 750 million genome-matched sequences. These massive datasets lead to the identification of new miRNAs, as well as new regulation of known miRNAs and their target genes during senescence, many of which have established roles in nutrient responsiveness and cell structural integrity. In keeping with remobilization of nutrients thought to occur during senescence, many miRNAs and targets had opposite expression pattern changes between leaf and silique tissues during the progression of senescence. Taken together, these findings highlight the integral role that miRNAs may play in the remobilization of resources and alteration of cellular structure that is known to occur in senescence. Experiments were initiated for functional analysis of specific senescence-associated miRNAs and respective target genes. Transgenic Arabidopsis plants were generated in which miR408, found in this study to be significantly induced in leaf senescence, was over-expressed either constitutively or under a senescence-specific promoter. These plants are currently being characterized for any altered phenotypes. In addition T-DNA knock out mutants for various target genes identified in this research are being analyzed. This work provides insights about specific miRNAs that contribute to leaf and silique senescence. The knowledge generated may suggest new strategies to monitor and alter the progression of senescence in crops for agricultural improvement.

Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

Tennant, David. Business Surveys on the Impact of COVID-19 on Jamaican Firms. Inter-American Development Bank, May 2021. http://dx.doi.org/10.18235/0003251.

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The datasets come from two surveys of Jamaican businesses conducted between May and June 2020. Two sets of self-administered surveys were conducted using Survey Monkey. A very small sample of financial institutions was surveyed to gain perspective on the challenges facing financiers as a result of the pandemic, and their efforts to respond to such challenges. Nine financial institutions completed this survey, and the results were used to complement the information derived from the second and major survey. The second survey targeted non-financial businesses operating in Jamaica. The sample of firms was selected from a list of all registered Jamaican firms, obtained from the Companies Office of Jamaica. A stratified random sample was used based on firm type, region, and sector. Some firms may have also participated in the study through contact made by their respective affiliations, which were approached to endorse the study and encourage their members to engage. A total of 390 firms completed the second survey. A significant degree of representation was achieved across size, type and age of business, sector and location of operation. Good gender representation was also achieved.

Renaud, Alexander, Michael Forte, Nicholas Spore, Brittany Bruder, Katherine Brodie, Jessamin Straub, and Jeffrey Ruby. Evaluation of Unmanned Aircraft Systems for flood risk management : results of terrain and structure assessments. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45000.

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The 2017 Duck Unmanned Aircraft Systems (UAS) Pilot Experiment was conducted by the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory, Field Research Facility (FRF), to assess the potential for different UAS to support US Army Corps of Engineers coastal and flood risk management. By involving participants from multiple ERDC laboratories, federal agencies, academia, and private industry, the work unit leads were able to leverage assets, resources, and expertise to assess data from multiple UAS. This report compares datasets from several UAS to assess their potential to survey and observe coastal terrain and structures. In this report, UAS data product accuracy was analyzed within the context of three potential applications: (1) general coastal terrain survey accuracy across the FRF property; (2) small-scale feature detection and observation within the experiment infrastructure area; and (3) accuracy for surveying coastal foredunes. The report concludes by presenting tradeoffs between UAS accuracy and the cost to operate to aid in selection of the best UAS for a particular task. While the technology and exact UAS models vary through time, the lessons learned from this study illustrate that UAS are available at a variety of costs to satisfy varying coastal management data needs.

Ruby, Jeffrey, Richard Massaro, John Anderson, and Robert Fischer. Three-dimensional geospatial product generation from tactical sources, co-registration assessment, and considerations. Engineer Research and Development Center (U.S.), February 2023. http://dx.doi.org/10.21079/11681/46442.

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According to Army Multi-Domain Operations (MDO) doctrine, generating timely, accurate, and exploitable geospatial products from tactical platforms is a critical capability to meet threats. The US Army Corps of Engineers, Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL) is carrying out 6.2 research to facilitate the creation of three-dimensional (3D) products from tactical sensors to include full-motion video, framing cameras, and sensors integrated on small Unmanned Aerial Systems (sUAS). This report describes an ERDC-GRL processing pipeline comprising custom code, open-source software, and commercial off-the-shelf (COTS) tools to geospatially rectify tactical imagery to authoritative foundation sources. Four datasets from different sensors and locations were processed against National Geospatial-Intelligence Agency–supplied foundation data. Results showed that the co-registration of tactical drone data to reference foundation varied from 0.34 m to 0.75 m, exceeding the accuracy objective of 1 m described in briefings presented to Army Futures Command (AFC) and the Assistant Security of the Army for Acquisition, Logistics and Technology (ASA(ALT)). A discussion summarizes the results, describes steps to address processing gaps, and considers future efforts to optimize the pipeline for generation of geospatial data for specific end-user devices and tactical applications.