A tensorized multitask deep learning network for progression prediction of Alzheimer's disease
S Tabarestani, M Eslami, M Cabrerizo… - Frontiers in aging …, 2022 - frontiersin.org
With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most
studies have focused on either identifying the subject's status through classification …
studies have focused on either identifying the subject's status through classification …
Indefinite core vector machine
FM Schleif, P Tino - Pattern Recognition, 2017 - Elsevier
The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier
for indefinite learning problems, but with quadratic to cubic complexity and a non-sparse …
for indefinite learning problems, but with quadratic to cubic complexity and a non-sparse …
Sparsification of core set models in non-metric supervised learning
Supervised learning employing positive semi definite kernels has gained wide attraction and
lead to a variety of successful machine learning approaches. The restriction to positive semi …
lead to a variety of successful machine learning approaches. The restriction to positive semi …
Sparsification of indefinite learning models
The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier
for indefinite learning problems, but with a non-sparse decision function. This very dense …
for indefinite learning problems, but with a non-sparse decision function. This very dense …
Machine Learning for Multiclass Classification and Prediction of Alzheimer's Disease
S Tabarestani - 2021 - digitalcommons.fiu.edu
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a common form
of dementia. This research aims to develop machine learning algorithms that diagnose and …
of dementia. This research aims to develop machine learning algorithms that diagnose and …