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Gini Based Learning for the Classification of Alzheimer's Disease and Features Identification with Automatic RGB Segmentation Algorithm.

, , , and . ICCSA (2), volume 10961 of Lecture Notes in Computer Science, page 92-106. Springer, (2018)

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Gini Based Learning for the Classification of Alzheimer's Disease and Features Identification with Automatic RGB Segmentation Algorithm., , , and . ICCSA (2), volume 10961 of Lecture Notes in Computer Science, page 92-106. Springer, (2018)Deviation-based Dynamic Time Warping for Clustering Human Sleep., , , and . BIOSIGNALS, page 88-95. SciTePress, (2016)Predicting Outcome of Ischemic Stroke Patients using Bootstrap Aggregating with M5 Model Trees., , , and . HEALTHINF, page 178-187. SciTePress, (2017)Modeling and Clustering of Human Sleep Time Series Using Dynamic Time Warping: Sequential and Distributed Implementations., , , and . BIOSTEC (Selected Papers), volume 690 of Communications in Computer and Information Science, page 276-294. Springer, (2016)Mining Statistically Significant Associations for Exploratory Analysis of Human Sleep Data., , , and . IEEE Trans. Information Technology in Biomedicine, 10 (3): 440-450 (2006)Mining Associations over Human Sleep Time Series., , , and . CBMS, page 323-328. IEEE Computer Society, (2005)Semi-Markov Modeling-Clustering of Human Sleep with Efficient Initialization and Stopping., , , and . BIOSIGNALS, page 61-68. SciTePress, (2014)Regression, Classification and Ensemble Machine Learning Approaches to Forecasting Clinical Outcomes in Ischemic Stroke., , , and . BIOSTEC (Selected Papers), volume 881 of Communications in Computer and Information Science, page 376-402. Springer, (2017)Comparison of Deep Learning and Support Vector Machine Learning for Subgroups of Multiple Sclerosis., , and . ICCSA (2), volume 10405 of Lecture Notes in Computer Science, page 142-153. Springer, (2017)Discovery of sleep composition types using expectation-maximization., , , , and . CBMS, page 26-31. IEEE Computer Society, (2010)