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.
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.
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.
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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Dynamics of neuronal firing correlation: modulation of "effective connectivity"
A. M. H. H. Aertsen;G. L. Gerstein;M. K. Habib;G. Palm.
Journal of Neurophysiology (1989)
On associative memory.
Biological Cybernetics (1980)
Three learning phases for radial-basis-function networks
Friedhelm Schwenker;Hans A. Kestler;Günther Palm.
Neural Networks (2001)
Density of neurons and synapses in the cerebral cortex of the mouse.
Almut Schüz;Günther Palm.
The Journal of Comparative Neurology (1989)
On the significance of correlations among neuronal spike trains.
G. Palm;A. M. H. J. Aertsen;G. L. Gerstein.
Biological Cybernetics (1988)
Theoretical Approaches to Complex Systems
Roland Heim;Günther Palm.
Symposium Theoretical Approaches to Complex Systems 1977 (1978)
Value-difference based exploration: adaptive control between epsilon-greedy and softmax
Michel Tokic;Günther Palm.
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence (2011)
Wörterbuch der Kognitionswissenschaft
G. Strube;B. Becker;C. Freksa;U. Hahn.
On representation and approximation of nonlinear systems
Biological Cybernetics (1978)
The Volterra Representation and the Wiener Expansion: Validity and Pitfalls
G. Palm;T. Poggio.
Siam Journal on Applied Mathematics (1977)
Profile was last updated on December 6th, 2021.
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