Results 21 to 30 of about 3,606,574 (279)
Knowledge Transfer Between Artificial Intelligence Systems
Frontiers in Neurorobotics, 2018 We consider the fundamental question: how a legacy “student” Artificial Intelligent (AI) system could learn from a legacy “teacher” AI system or a human expert without re-training and, most importantly, without requiring significant computational ...
Ivan Y. Tyukin +6 more
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HIGH-DIMENSIONAL DATA ANALYSIS [PDF]
, 2010 High-Dimensional Classification: High-Dimensional Classification (J-Q Fan et al.) Flexible Large Margin Classifiers (Y-F Liu & Y-C Wu) Large-Scale Multiple Testing: Large-Scale Multiple Testing (T T Cai & W-G Sun) Model Building with Variable Selection: Model Building with Variable Selection (M Yuan) Bayesian Variable Selection in Regression with ...
Tony Cai, Xiaotong Shen
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High-Dimensional Matched Subspace Detection When Data are Missing [PDF]
, 2010 We consider the problem of deciding whether a highly incomplete signal lies within a given subspace. This problem, Matched Subspace Detection, is a classical, well-studied problem when the signal is completely observed. High- dimensional testing problems
Balzano, Laura +2 more
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The properties of nonuniformity analysis of high dimensional data
Lietuvos Matematikos Rinkinys, 2004 A novel approach to outlier detection and clustering on the ground of the distribution of distances between multidimensional points is presented. The basic idea is to eval uate the outlier factor for each data point.
Vydūnas Šaltenis
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Data integration with high dimensionality
Biometrika, 2017 SummaryWe consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured.
Gao, Xin, Carroll, Raymond J.
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RLE plots: Visualizing unwanted variation in high dimensional data. [PDF]
PLoS ONE, 2018 Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data.
Luke C Gandolfo, Terence P Speed
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Tests for covariance matrices, particularly for high dimensional data [PDF]
, 2010 Test statistics for sphericity and identity of the covariance matrix are presented, when the data are multivariate normal and the dimension, p, can be larger than the sample size, n.
Ahmad, M. Rauf
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Nonparametric Tests Applicable to High Dimensional Data
Austrian Journal of Statistics, 2019 Constructions of data driven ordering of set of multivariate observations are presented. The methods employ also dissimilarity measures. The ranks are used in the construction of test statistics for location problem and in the construction of the ...
Frantisek Rublik
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High Dimensional Data Clustering using Self-Organized Map
Knowledge Engineering and Data Science, 2019 As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data
Ruth Ema Febrita +2 more
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Entropy, 2022 Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).
Evgeny M. Mirkes +5 more
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