What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Lars Kai Hansen mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Independent component analysis.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Voxel, Data mining, Brain mapping and Test set.
His Machine learning study combines topics from a wide range of disciplines, such as Superposition principle, Multilinear principal component analysis, Invariant and Convex hull.
His work carried out in the field of Artificial neural network brings together such families of science as Tensor, Current and Pattern recognition.
His Independent component analysis research is multidisciplinary, incorporating perspectives in Electroencephalography, Toolbox, Speech recognition, Functional magnetic resonance imaging and Algorithm.
His studies deal with areas such as Fault tolerance, Regularization and Cross-validation as well as Generalization error.
His most cited work include:
- Neural network ensembles (3175 citations)
- Neural Networks for Modelling and Control of Dynamic Systems (819 citations)
- On Clustering fMRI Time Series (408 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Algorithm.
His research integrates issues of Natural language processing, Computer vision and Electroencephalography in his study of Artificial intelligence.
He has included themes like Speech recognition, Neuroimaging and Cluster analysis in his Pattern recognition study.
His studies in Artificial neural network integrate themes in fields like Generalization and Pruning.
His research on Algorithm frequently connects to adjacent areas such as Blind signal separation.
He most often published in these fields:
- Artificial intelligence (48.83%)
- Pattern recognition (25.05%)
- Machine learning (17.41%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (48.83%)
- Electroencephalography (9.55%)
- Pattern recognition (25.05%)
In recent papers he was focusing on the following fields of study:
Lars Kai Hansen mostly deals with Artificial intelligence, Electroencephalography, Pattern recognition, Machine learning and Artificial neural network.
His study looks at the relationship between Artificial intelligence and fields such as Functional magnetic resonance imaging, as well as how they intersect with chemical problems.
His Electroencephalography study integrates concerns from other disciplines, such as Stimulus, Attentional modulation, Speech recognition and Neuroimaging.
His study on Pattern recognition also encompasses disciplines like
- Prior probability that intertwine with fields like Computational complexity theory,
- Inverse problem that intertwine with fields like Mathematical optimization.
The Machine learning study combines topics in areas such as Sleep, Schizophrenia, Sleep apnea, Narcolepsy and Set.
His Artificial neural network study combines topics in areas such as Theoretical computer science, Feature engineering, Class, Current and Convolutional neural network.
Between 2013 and 2021, his most popular works were:
- Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains. (117 citations)
- The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System (81 citations)
- Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation (70 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Lars Kai Hansen spends much of his time researching Electroencephalography, Artificial intelligence, Pattern recognition, Algorithm and Sleep Stages.
The various areas that Lars Kai Hansen examines in his Electroencephalography study include Machine learning, Neuroimaging, Temporal resolution and Human–computer interaction.
His Machine learning research incorporates elements of Cerebral cortex, Data-driven and Image resolution.
His study brings together the fields of Data acquisition and Artificial intelligence.
In his study, Computational complexity theory, Inference, Covariance and Brain mapping is strongly linked to Inverse problem, which falls under the umbrella field of Pattern recognition.
His Algorithm research is multidisciplinary, incorporating elements of Probability distribution, Latent variable, Cluster analysis, Curvature and Gibbs sampling.
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