Results 41 to 50 of about 1,253,621 (308)
Clustering Evaluation in High-Dimensional Data [PDF]
, 2016 Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that
Nenad Tomašev, Miloš Radovanović
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Recession forecasting with high‐dimensional data [PDF]
Journal of Forecasting, 2021 AbstractIn this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost‐sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable.
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Variable Selection and Parameter Tuning in High-Dimensional Prediction [PDF]
, 2010 In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection.
Boulesteix, Anne-Laure +1 more
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, 2023 Receiver function data used in "Three-Dimensional Receiver Function Adjoint Tomography for High-resolution Seismic Array Imaging: Methodology and Applications in Southeastern ...
Xu, Mijian +4 more
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An efficient predictive analytics system for high dimensional big data
Journal of King Saud University: Computer and Information Sciences, 2022 The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data.
Myat Cho Mon Oo, Thandar Thein
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FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
PaMPa-HD: A Parallel MapReduce-Based Frequent Pattern Miner for High-Dimensional Data [PDF]
, 2015 Frequent closed itemset mining is among the most complex exploratory techniques in data mining, and provides the ability to discover hidden correlations in transactional datasets.
Pulvirenti, Fabio +11 more
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Outlier Detection in High Dimensional Data [PDF]
Journal of Information & Knowledge Management, 2020 High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features.
Firuz Kamalov, Ho Hon Leung
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FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
Nicola17/High-Dimensional-Inspector: First Release
, 2018 <p>Library for the scalable analysis of large high-dimensional data.</p> <p>It contains the A-tSNE and HSNE algorithms.</p ...
Thomas Höllt, Nicola Pezzotti
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