What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Carlos G. Puntonet mainly focuses on Artificial intelligence, Pattern recognition, Support vector machine, Algorithm and Computer-aided diagnosis.
His Artificial intelligence research incorporates themes from Machine learning, Likelihood-ratio test, Blind signal separation and Estimator.
His Pattern recognition research integrates issues from Bispectrum and Voice activity detection.
He combines subjects such as Feature extraction, Feature selection and Voxel with his study of Support vector machine.
His studies deal with areas such as Fixed point, Artificial neural network, Linear independence, Infomax and Mathematical optimization as well as Algorithm.
His research in Computer-aided diagnosis tackles topics such as Disease which are related to areas like Neuroimaging.
His most cited work include:
- 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis (111 citations)
- A Geometric Algorithm for Overcomplete Linear ICA (93 citations)
- A Novel LMS Algorithm Applied to Adaptive Noise Cancellation (85 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Independent component analysis, Pattern recognition, Algorithm and Blind signal separation are his primary areas of study.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
The Independent component analysis study combines topics in areas such as Simulated annealing, Higher-order statistics, Preprocessor and Acoustic emission.
His Pattern recognition research includes elements of Speech recognition and Voxel.
His study explores the link between Algorithm and topics such as Series that cross with problems in Hilbert–Huang transform.
Maxima and minima is closely connected to Genetic algorithm in his research, which is encompassed under the umbrella topic of Blind signal separation.
He most often published in these fields:
- Artificial intelligence (45.05%)
- Independent component analysis (34.47%)
- Pattern recognition (33.79%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (45.05%)
- Pattern recognition (33.79%)
- Support vector machine (11.60%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Support vector machine, Computer-aided diagnosis and Algorithm.
His research in Artificial intelligence is mostly concerned with Voxel.
Carlos G. Puntonet has researched Pattern recognition in several fields, including Kullback–Leibler divergence and Data set.
His work deals with themes such as Neuroimaging, Clinical psychology, Positron emission tomography, Random forest and Feature selection, which intersect with Support vector machine.
Carlos G. Puntonet interconnects Supervised learning and Feature vector in the investigation of issues within Computer-aided diagnosis.
His Algorithm study integrates concerns from other disciplines, such as Basis, Hilbert–Huang transform, Series and Signal processing.
Between 2010 and 2021, his most popular works were:
- 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis (111 citations)
- Brain connectivity analysis: a short survey (69 citations)
- Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test (61 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Support vector machine, Pattern recognition, Feature selection and Computer-aided diagnosis.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His Support vector machine research includes themes of Multivariate statistics, Neuroimaging, Clinical psychology and Pairwise comparison.
His Pattern recognition study combines topics from a wide range of disciplines, such as Rendering and Test set.
His Feature selection research focuses on subjects like Voxel, which are linked to Positron emission tomography, Principal component analysis, Association rule learning and Training set.
His Computer-aided diagnosis research focuses on Disease and how it connects with Feature vector and Supervised learning.
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