scholarly journals A Robust Approach to Variation in Carpathian Rusyn: Resampling-Based Methods for Small Data Sets

2021 ◽  
Vol 72 (2) ◽  
pp. 603-617
Author(s):  
Moulay Zaidan Lahjouji-Seppälä ◽  
Achim Rabus
Keyword(s):  
Large Scale ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Large Scale Data ◽  
Dependent Variables ◽  
Small Data Sets ◽  
The Stability ◽  
Sociolinguistic Status ◽  
Scale Data

Abstract Quantitative, corpus based research on spontaneous spoken Carpathian Rusyn language can cause several data-related problems: Speakers are using ambivalent forms in different quantities, resulting in a biased data set – while a stricter data-cleaning process would lead to a large scale data loss. On top of that, polytomous categorical dependent variables are hard to analyze due to methodological limitations. This paper provides several approaches to face unbalanced and biased data sets containing variation of conjugational forms of the verb maty ‘to have’ and (po-)znaty ‘to know’ in Carpathian Rusyn language. Using resampling based methods like Cross-Validation, Bootstrapping and Random Forests, we provide a strategy for circumventing possible methodological pitfalls and gaining the most information from our precious data, without trying to p-hack the results. Calculating the predictive power of several sociolinguistic factors on linguistic variation, we can make valid statements about the (sociolinguistic) status of Rusyn and the stability of the old dialect continuum of Rusyn varieties.

scholarly journals GeoBoost: An Incremental Deep Learning Approach toward Global Mapping of Buildings from VHR Remote Sensing Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1794
Author(s):  
Naisen Yang ◽  
Hong Tang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Data Sets ◽  
Data Set ◽  
Large Scale Data ◽  
Learning Tasks ◽  
Global Mapping ◽  
Scale Data

Modern convolutional neural networks (CNNs) are often trained on pre-set data sets with a fixed size. As for the large-scale applications of satellite images, for example, global or regional mappings, these images are collected incrementally by multiple stages in general. In other words, the sizes of training datasets might be increased for the tasks of mapping rather than be fixed beforehand. In this paper, we present a novel algorithm, called GeoBoost, for the incremental-learning tasks of semantic segmentation via convolutional neural networks. Specifically, the GeoBoost algorithm is trained in an end-to-end manner on the newly available data, and it does not decrease the performance of previously trained models. The effectiveness of the GeoBoost algorithm is verified on the large-scale data set of DREAM-B. This method avoids the need for training on the enlarged data set from scratch and would become more effective along with more available data.

scholarly journals LARGE‐SCALE DATA VISUALIZATION WITH MISSING VALUES

2006 ◽  
Vol 12 (1) ◽  
pp. 44-49
Author(s):  
Sergiy Popov
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Missing Values ◽  
Original Data ◽  
Nonlinear Dimensionality Reduction ◽  
Data Sets ◽  
Data Set ◽  
Large Scale Data ◽  
Learning Procedure ◽  
Scale Data

Visualization of large‐scale data inherently requires dimensionality reduction to 1D, 2D, or 3D space. Autoassociative neural networks with a bottleneck layer are commonly used as a nonlinear dimensionality reduction technique. However, many real‐world problems suffer from incomplete data sets, i.e. some values can be missing. Common methods dealing with missing data include the deletion of all cases with missing values from the data set or replacement with mean or “normal” values for specific variables. Such methods are appropriate when just a few values are missing. But in the case when a substantial portion of data is missing, these methods can significantly bias the results of modeling. To overcome this difficulty, we propose a modified learning procedure for the autoassociative neural network that directly takes the missing values into account. The outputs of the trained network may be used for substitution of the missing values in the original data set.

scholarly journals A comparative study of Distributed Large Scale Data Mining Algorithms

2020 ◽  
Author(s):  
Isha Sood ◽  
Varsha Sharma
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Large Scale Data ◽  
Mapreduce Model ◽  
Mining Algorithms ◽  
Scale Data

Essentially, data mining concerns the computation of data and the identification of patterns and trends in the information so that we might decide or judge. Data mining concepts have been in use for years, but with the emergence of big data, they are even more common. In particular, the scalable mining of such large data sets is a difficult issue that has attached several recent findings. A few of these recent works use the MapReduce methodology to construct data mining models across the data set. In this article, we examine current approaches to large-scale data mining and compare their output to the MapReduce model. Based on our research, a system for data mining that combines MapReduce and sampling is implemented and addressed

Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood

2013 ◽  
Vol 17 (1) ◽  
pp. 60-73
Author(s):  
Hao Liu ◽  
◽  
Satoshi Oyama ◽  
Masahito Kurihara ◽  
Haruhiko Sato
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Large Scale Data ◽  
Density Based Clustering ◽  
Scale Data ◽  
Large Scale Data Sets

Clustering is an important tool for data analysis and many clustering techniques have been proposed over the past years. Among them are density-based clustering methods, which have several benefits such as the number of clusters is not required before carrying out clustering; the detected clusters can be represented in an arbitrary shape and outliers can be detected and removed. Recently, the density-based algorithms were extended with the fuzzy set theory, which has made these algorithm more robust. However, the density-based clustering algorithms usually require a time complexity ofO(n2) wherenis the number of data in the data set, implying that they are not suitable to work with large scale data sets. In this paper, a novel clustering algorithm called landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN) is proposed. The concept, landmark, is used to represent a subset of the input data set which makes the algorithm efficient on large scale data sets. We give a theoretical analysis on time complexity and space complexity, which shows both of them are linear to the size of the data set. The experiments show that the landmark FN-DBSCAN is much faster than FN-DBSCAN and provides a very good quality of clustering.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

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