What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Friedhelm Schwenker mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Semi-supervised learning and Speech recognition.
His study in Support vector machine, Supervised learning, Artificial neural network, Classifier and Fuzzy logic is done as part of Artificial intelligence.
His Machine learning research is multidisciplinary, incorporating elements of Hidden Markov model, Human–computer interaction and Robustness.
His Pattern recognition research includes themes of Feature and Wiener filter.
Friedhelm Schwenker combines subjects such as Stability, Active learning and Cluster analysis with his study of Semi-supervised learning.
His Speech recognition study combines topics in areas such as Mixture model, Harmonic wavelet transform and Facial expression.
His most cited work include:
- Three learning phases for radial-basis-function networks (347 citations)
- Pattern classification and clustering: A review of partially supervised learning approaches (132 citations)
- Iterative retrieval of sparsely coded associative memory patterns (89 citations)
What are the main themes of his work throughout his whole career to date?
Friedhelm Schwenker mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Classifier.
His work investigates the relationship between Artificial intelligence and topics such as Speech recognition that intersect with problems in Mixture model.
His study looks at the relationship between Pattern recognition and topics such as Cluster analysis, which overlap with Data mining.
His research on Artificial neural network frequently links to adjacent areas such as Deep learning.
His study in Classifier is interdisciplinary in nature, drawing from both Decision fusion, Affective computing and Categorization.
His Supervised learning research incorporates elements of Co-training and Unsupervised learning.
He most often published in these fields:
- Artificial intelligence (79.44%)
- Pattern recognition (44.95%)
- Machine learning (36.24%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (79.44%)
- Pattern recognition (44.95%)
- Machine learning (36.24%)
In recent papers he was focusing on the following fields of study:
Friedhelm Schwenker focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Classifier.
Segmentation, Support vector machine, Convolutional neural network, Feature extraction and Artificial neural network are the core of his Artificial intelligence study.
His research in Support vector machine intersects with topics in Small set and Leverage.
In his research, Pooling, Mutual information, Ensemble forecasting and Perceptron is intimately related to Feature selection, which falls under the overarching field of Artificial neural network.
His studies in Pattern recognition integrate themes in fields like Facial expression and Bengali.
His Machine learning study incorporates themes from Robustness and Set.
Between 2018 and 2021, his most popular works were:
- Multi-modal Pain Intensity Recognition based on the SenseEmotion Database (23 citations)
- A dataset of continuous affect annotations and physiological signals for emotion analysis. (15 citations)
- Exploring Deep Physiological Models for Nociceptive Pain Recognition. (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Friedhelm Schwenker mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Database and Convolutional neural network.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Valence and Signal processing.
His study in the field of Classifier is also linked to topics like Set.
His Segmentation study combines topics in areas such as Histogram, k-means clustering, Fuzzy logic, Algorithm and Convolution.
The study incorporates disciplines such as Affective computing and Data set in addition to Database.
His work deals with themes such as Autoencoder, Information fusion and Noise reduction, which intersect with Convolutional neural network.
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