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Development of Gaussian Learning Algorithms for Early Detection of Alzheimer's Disease
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In: FIU Electronic Theses and Dissertations (2020)
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| 2 |
Cognitive discriminative feature selection using variance fractal dimension for the detection of cyber attacks
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| 3 |
How to visualize high-dimensional data: a roadmap
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In: Journal of Data Mining and Digital Humanities, Vol Special issue on Visualisations in Historical Linguistics (2020) (2020)
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Abstract:
International audience Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis.
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Keyword:
[shs]humanities and social sciences; AZ20-999; Bibliography. Library science. Information resources; cluster analysis; data visualization; dimensionality reduction; high dimensionality; History of scholarship and learning. The humanities; multivariate data; Z
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URL: https://doaj.org/article/7655036ba85642e9901cfbbfc0a6bf5e
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| 4 |
Investigating Gender-biased Items in a High-stake Language Proficiency Test: Using the Rasch Model Measurement
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In: Applied Linguistics Research Journal, Vol 4, Iss 5, Pp 1-21 (2020) (2020)
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| 5 |
Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats
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Siddiqui, Sana. - : ACM (IWSPA), 2017. : IEEE (IJCNN), 2017. : Springer, 2017. : IEEE (ICCI*CC), 2017
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| 6 |
Using Differential Item Functioning to Investigate the Impact of Reading Proficiency on Science Achievement
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| 9 |
Effect of Tuned Parameters on a LSA Multiple Choice Questions Answering Model
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-00336126 ; Behavior Research Methods, Psychonomic Society, Inc, 2009, 41 (4), pp.1201--1209. ⟨10.3758/BRM.41.4.1201⟩ (2009)
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| 10 |
Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology
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In: http://www.cs.toronto.edu/~gh/Courses/2528/Readings/2002f/Lai+Wu.pdf (2002)
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| 11 |
Medical Data Mining on the Internet: Research on a Cancer Information System
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In: DTIC (1999)
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| 12 |
Dimensionality reduction of electropalatographic data using latent variable models
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| 13 |
Statistical Methods for Neural Network Prediction Models
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In: https://hal.archives-ouvertes.fr/hal-03235964 ; [Research Report] Control Systems Group School of Electronic Engineering. Dublin City University. 1997 (1997)
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| 14 |
Analysis of Defense Language Institute Automated Student Questionnaire Data.
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In: DTIC AND NTIS (1996)
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