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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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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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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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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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Abstract:
International audience ; This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [SCCO.PSYC]Cognitive science/Psychology; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; Latent Semantic Analysis (LSA); LSA software; MCQ Answering Model; Semantic similarity; Unsupervised Learning; Vector Space Model Dimensionality
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URL: https://doi.org/10.3758/BRM.41.4.1201
https://hal.archives-ouvertes.fr/hal-00336126v3/file/eLSA1-brm20.pdf
https://hal.archives-ouvertes.fr/hal-00336126
https://hal.archives-ouvertes.fr/hal-00336126v3/document
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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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