What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Nathan Intrator mainly focuses on Artificial intelligence, Artificial neural network, Machine learning, Feature extraction and Pattern recognition.
His work carried out in the field of Artificial intelligence brings together such families of science as Theoretical computer science, Perception, Computer vision and Pattern recognition.
His Artificial neural network study combines topics in areas such as Regularization, Algorithm, Detection theory, Ensemble averaging and Statistics.
His Machine learning research incorporates themes from Regression problems and Maxima and minima.
The concepts of his Feature extraction study are interwoven with issues in Unsupervised learning, Projection pursuit and Dimensionality reduction.
Nathan Intrator interconnects Normalization, Overfitting, Word, Lexicon and Facial recognition system in the investigation of issues within Pattern recognition.
His most cited work include:
- Free water elimination and mapping from diffusion MRI. (454 citations)
- Bootstrapping with Noise: An Effective Regularization Technique (186 citations)
- Invited Article: Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions (183 citations)
What are the main themes of his work throughout his whole career to date?
Nathan Intrator mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Machine learning.
His work in Feature extraction, Unsupervised learning, Dimensionality reduction, Projection pursuit and Classifier is related to Artificial intelligence.
In his work, Audiology is strongly intertwined with Electroencephalography, which is a subfield of Pattern recognition.
His Computer vision study combines topics from a wide range of disciplines, such as Acoustic camera, Receptive field and Sonar.
The study incorporates disciplines such as Regularization, Statistics, Data set and Facial recognition system in addition to Artificial neural network.
His Machine learning study deals with Generalization intersecting with Embedding, Contextual image classification and Representation.
He most often published in these fields:
- Artificial intelligence (54.64%)
- Pattern recognition (26.23%)
- Computer vision (19.67%)
What were the highlights of his more recent work (between 2010-2021)?
- Electroencephalography (11.48%)
- Artificial intelligence (54.64%)
- Brain activity and meditation (6.01%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Electroencephalography, Artificial intelligence, Brain activity and meditation, Pattern recognition and Functional magnetic resonance imaging.
His Electroencephalography study incorporates themes from Visual perception and Cognition.
His research in Artificial intelligence intersects with topics in Machine learning, Kinematics and Computer vision.
Nathan Intrator works mostly in the field of Pattern recognition, limiting it down to concerns involving Schizophrenia and, occasionally, Event-related potential.
His study on Functional magnetic resonance imaging also encompasses disciplines like
- Brain mapping, which have a strong connection to Communication, Darkness, Rhythm, Sensory system and Eye movement,
- Neuroimaging that intertwine with fields like Support vector machine and Voxel.
In his study, which falls under the umbrella issue of Signal processing, Feature extraction is strongly linked to Data mining.
Between 2010 and 2021, his most popular works were:
- Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation (51 citations)
- An EEG Finger-Print of fMRI deep regional activation. (39 citations)
- Machine learning fMRI classifier delineates subgroups of schizophrenia patients (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Electroencephalography, Neuroscience, Functional magnetic resonance imaging, Amygdala and Neurofeedback.
His studies in Electroencephalography integrate themes in fields like Schizophrenia and Signal processing.
His Signal processing research incorporates elements of Biomedicine, Feature extraction, Systems biology and Smart device.
His biological study focuses on Visual perception.
Nathan Intrator combines subjects such as Audiology, Working memory, n-back, Classifier and Pattern analysis with his study of Functional magnetic resonance imaging.
Nathan Intrator has researched Brain activity and meditation in several fields, including Image resolution, Data mining, Time–frequency analysis, Speech recognition and Temporal resolution.
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