D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 10,348 142 World Ranking 5466 National Ranking 20

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

His most cited work include:

  • 2012 Special Issue: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition (693 citations)
  • Covariance Matrix Adaptation for Multi-objective Optimization (560 citations)
  • The German Traffic Sign Recognition Benchmark: A multi-class classification competition (424 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (55.66%)
  • Machine learning (25.94%)
  • Pattern recognition (20.28%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (55.66%)
  • Pattern recognition (20.28%)
  • Segmentation (6.60%)

In recent papers he was focusing on the following fields of study:

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.

Between 2018 and 2021, his most popular works were:

  • One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation (23 citations)
  • An unexpectedly large count of trees in the West African Sahara and Sahel (20 citations)
  • U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (15 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

1078 Citations

Covariance Matrix Adaptation for Multi-objective Optimization

Christian Igel;Nikolaus Hansen;Stefan Roth.
Evolutionary Computation (2007)

781 Citations

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)

685 Citations

Evolutionary tuning of multiple SVM parameters

Frauke Friedrichs;Christian Igel.
Neurocomputing (2005)

587 Citations

An Introduction to Restricted Boltzmann Machines

Asja Fischer;Asja Fischer;Christian Igel.
iberoamerican congress on pattern recognition (2012)

577 Citations

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)

558 Citations

Empirical evaluation of the improved Rprop learning algorithms

Christian Igel;Michael Hüsken.
Neurocomputing (2003)

506 Citations

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)

506 Citations

Improving the Rprop Learning Algorithm

Christian Igel;Michael Husken.
(2000)

494 Citations

Training restricted Boltzmann machines

Asja Fischer;Christian Igel.
Pattern Recognition (2014)

474 Citations

Best Scientists Citing Christian Igel

César Hervás-Martínez

César Hervás-Martínez

University of Córdoba

Publications: 46

Nikolaus Hansen

Nikolaus Hansen

École Polytechnique

Publications: 31

Yaochu Jin

Yaochu Jin

University of Surrey

Publications: 29

Marc Schoenauer

Marc Schoenauer

French Institute for Research in Computer Science and Automation - INRIA

Publications: 25

Qingfu Zhang

Qingfu Zhang

City University of Hong Kong

Publications: 25

Tobias Friedrich

Tobias Friedrich

Hasso Plattner Institute

Publications: 22

Carlos A. Coello Coello

Carlos A. Coello Coello

CINVESTAV

Publications: 21

Jürgen Schmidhuber

Jürgen Schmidhuber

Dalle Molle Institute for Artificial Intelligence Research

Publications: 21

Bernhard Sendhoff

Bernhard Sendhoff

Honda (Germany)

Publications: 20

Leonardo Vanneschi

Leonardo Vanneschi

Universidade Nova de Lisboa

Publications: 18

Thomas Bäck

Thomas Bäck

Leiden University

Publications: 17

Purang Abolmaesumi

Purang Abolmaesumi

University of British Columbia

Publications: 17

Xin Yao

Xin Yao

Southern University of Science and Technology

Publications: 16

Holger R. Roth

Holger R. Roth

Nvidia (United States)

Publications: 16

Pheng-Ann Heng

Pheng-Ann Heng

Chinese University of Hong Kong

Publications: 16

Jean-Baptiste Mouret

Jean-Baptiste Mouret

University of Lorraine

Publications: 15

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.