What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Günther Palm focuses on Artificial intelligence, Artificial neural network, Content-addressable memory, Associative property and Algorithm.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Speech recognition and Pattern recognition.
His studies in Pattern recognition integrate themes in fields like Stimulus, Similarity, Data pre-processing and Cluster analysis.
His research in Artificial neural network intersects with topics in Neuron and Benchmark.
His research in the fields of Bidirectional associative memory overlaps with other disciplines such as Bit.
His Associative property research integrates issues from Cognitive science, Binary number, Computational model and Pruning.
His most cited work include:
- Dynamics of neuronal firing correlation: modulation of "effective connectivity" (924 citations)
- On associative memory. (389 citations)
- Three learning phases for radial-basis-function networks (347 citations)
What are the main themes of his work throughout his whole career to date?
Günther Palm mainly focuses on Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Speech recognition.
Günther Palm regularly links together related areas like Computer vision in his Artificial intelligence studies.
The study incorporates disciplines such as Algorithm, Associative property and Neural coding in addition to Artificial neural network.
His study in Support vector machine, Feature extraction, Learning vector quantization, Pattern recognition and Convolutional neural network is carried out as part of his Pattern recognition studies.
His biological study spans a wide range of topics, including Dempster–Shafer theory and Data mining.
His Content-addressable memory study integrates concerns from other disciplines, such as Stimulus and Hebbian theory.
He most often published in these fields:
- Artificial intelligence (55.17%)
- Artificial neural network (22.13%)
- Pattern recognition (21.84%)
What were the highlights of his more recent work (between 2010-2020)?
- Artificial intelligence (55.17%)
- Machine learning (19.25%)
- Pattern recognition (21.84%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Classifier.
Günther Palm interconnects Fusion and Computer vision in the investigation of issues within Artificial intelligence.
He works mostly in the field of Machine learning, limiting it down to topics relating to Emotion recognition and, in certain cases, Data collection, as a part of the same area of interest.
His Pattern recognition research is multidisciplinary, incorporating elements of MNIST database and Markov model.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Range and Pedestrian detection.
As a part of the same scientific family, he mostly works in the field of Neural coding, focusing on Bidirectional associative memory and, on occasion, Associative property.
Between 2010 and 2020, his most popular works were:
- Value-difference based exploration: adaptive control between epsilon-greedy and softmax (109 citations)
- Neural associative memories and sparse coding (88 citations)
- Multiple classifier systems for the classificatio of audio-visual emotional states (88 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Günther Palm mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Human–computer interaction.
His Artificial intelligence research integrates issues from Task and Fusion.
His work is dedicated to discovering how Fusion, Multiple classification are connected with Speech recognition and other disciplines.
His research in Machine learning intersects with topics in Information fusion, Cognition, State and Robustness.
His work on Classifier fusion and Normalization as part of general Pattern recognition research is frequently linked to Personalization and Modal, bridging the gap between disciplines.
His work carried out in the field of Artificial neural network brings together such families of science as Projection and Differentiable function.
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