Christian Igel mainly investigates Artificial intelligence, Machine learning, Mathematical optimization, Evolution strategy and CMA-ES. His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition. His research integrates issues of Breast cancer, Mammography and Kernel in his study of Pattern recognition.
His Machine learning research includes elements of Algorithm and Benchmark. His Mathematical optimization research incorporates themes from Support vector machine, Selection, Metric and Reinforcement learning. The concepts of his Stochastic neural network study are interwoven with issues in Markov chain Monte Carlo, Deep belief network, Graphical model, Markov chain and Boltzmann machine.
Artificial intelligence, Machine learning, Pattern recognition, Mathematical optimization and Evolution strategy are his primary areas of study. His study in Artificial neural network, Support vector machine, Evolutionary algorithm, Deep learning and Segmentation is carried out as part of his studies in Artificial intelligence. His Machine learning study frequently links to related topics such as Benchmark.
His work carried out in the field of Pattern recognition brings together such families of science as Kernel and Hyperparameter. His work on Multi-objective optimization and Optimization problem as part of general Mathematical optimization study is frequently linked to Vector optimization, bridging the gap between disciplines. His Evolution strategy study integrates concerns from other disciplines, such as Covariance matrix, Metric and Reinforcement learning.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Segmentation, Machine learning and Deep learning. His Artificial intelligence study frequently draws connections to adjacent fields such as Function. His research in the fields of Euclidean distance overlaps with other disciplines such as Domain.
His Segmentation research is multidisciplinary, incorporating perspectives in Clinical efficacy and Medical physics. He has included themes like Intensive care unit and Electrooculography in his Machine learning study. In his study, Natural language processing is inextricably linked to Class, which falls within the broad field of Artificial neural network.
Christian Igel spends much of his time researching Artificial intelligence, Deep learning, Image segmentation, Pattern recognition and Segmentation. His Artificial intelligence study incorporates themes from Clinical information, Dice and Implantable defibrillators. In most of his Deep learning studies, his work intersects topics such as Convolutional neural network.
Christian Igel combines subjects such as Overfitting and Hyperparameter with his study of Pattern recognition. His study in Hyperparameter is interdisciplinary in nature, drawing from both Artificial neural network, Time-series segmentation and Semi-supervised learning. Christian Igel interconnects Dental radiography, Radiography and Data mining in the investigation of issues within Segmentation.
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2012 Special Issue: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp;M. Schlipsing;J. Salmen;C. Igel.
Neural Networks (2012)
Covariance Matrix Adaptation for Multi-objective Optimization
Christian Igel;Nikolaus Hansen;Stefan Roth.
Evolutionary Computation (2007)
The German Traffic Sign Recognition Benchmark: A multi-class classification competition
Johannes Stallkamp;Marc Schlipsing;Jan Salmen;Christian Igel.
international joint conference on neural network (2011)
Evolutionary tuning of multiple SVM parameters
Frauke Friedrichs;Christian Igel.
Neurocomputing (2005)
An Introduction to Restricted Boltzmann Machines
Asja Fischer;Asja Fischer;Christian Igel.
iberoamerican congress on pattern recognition (2012)
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
Adhish Prasoon;Kersten Petersen;Christian Igel;François Lauze.
medical image computing and computer assisted intervention (2013)
Empirical evaluation of the improved Rprop learning algorithms
Christian Igel;Michael Hüsken.
Neurocomputing (2003)
Detection of traffic signs in real-world images: The German traffic sign detection benchmark
Sebastian Houben;Johannes Stallkamp;Jan Salmen;Marc Schlipsing.
international joint conference on neural network (2013)
Improving the Rprop Learning Algorithm
Christian Igel;Michael Husken.
(2000)
Training restricted Boltzmann machines
Asja Fischer;Christian Igel.
Pattern Recognition (2014)
University of Copenhagen
Max Planck Society
École Polytechnique
King's College London
Erasmus University Rotterdam
King's College London
Radboud University Nijmegen
Hasso Plattner Institute
University of Central Florida
Ruhr University Bochum
French Institute for Research in Computer Science and Automation - INRIA
Publications: 25
Profile was last updated on December 6th, 2021.
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The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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