H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 32 Citations 6,313 155 World Ranking 7085 National Ranking 3340

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer network

His primary scientific interests are in Artificial intelligence, Collective intelligence, Artificial neural network, Pattern recognition and Reinforcement learning. His Artificial intelligence study typically links adjacent topics like Machine learning. The study incorporates disciplines such as Mathematical optimization, Routing and Shortest path problem in addition to Collective intelligence.

His work on Classifier as part of general Pattern recognition study is frequently linked to Colposcopy and Cervical cancer, therefore connecting diverse disciplines of science. His research integrates issues of Bayes' theorem and Word error rate in his study of Classifier. His studies in Reinforcement learning integrate themes in fields like Game theory and Function.

His most cited work include:

  • Error Correlation and Error Reduction in Ensemble Classifiers (527 citations)
  • Analysis of decision boundaries in linearly combined neural classifiers (289 citations)
  • Classifier ensembles: Select real-world applications (221 citations)

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

His primary areas of investigation include Artificial intelligence, Multi-agent system, Reinforcement learning, Distributed computing and Artificial neural network. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition. His Multi-agent system research is multidisciplinary, relying on both Autonomous agent, Mathematical optimization and Process.

As part of one scientific family, Kagan Tumer deals mainly with the area of Reinforcement learning, narrowing it down to issues related to the Function, and often Control and Collective intelligence. Kagan Tumer has included themes like Task and Search and rescue in his Distributed computing study. His work carried out in the field of Classifier brings together such families of science as Order statistic, Bayes' theorem and Bayes error rate.

He most often published in these fields:

  • Artificial intelligence (48.56%)
  • Multi-agent system (31.28%)
  • Reinforcement learning (22.22%)

What were the highlights of his more recent work (between 2013-2020)?

  • Artificial intelligence (48.56%)
  • Multi-agent system (31.28%)
  • Reinforcement learning (22.22%)

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

His primary areas of study are Artificial intelligence, Multi-agent system, Reinforcement learning, Evolutionary algorithm and Machine learning. His study connects Process and Artificial intelligence. His Multi-agent system research is multidisciplinary, relying on both Mathematical optimization, Robot, Distributed computing and Control.

His Distributed computing research incorporates themes from Key, Task and Feed forward. His studies deal with areas such as Management science, Evaluation function, Credit assignment and Air traffic management as well as Evolutionary algorithm. In his study, Online algorithm is strongly linked to Benchmark, which falls under the umbrella field of Machine learning.

Between 2013 and 2020, his most popular works were:

  • Potential-based difference rewards for multiagent reinforcement learning (46 citations)
  • Evolution-Guided Policy Gradient in Reinforcement Learning (40 citations)
  • Evolution-Guided Policy Gradient in Reinforcement Learning (29 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer network

The scientist’s investigation covers issues in Artificial intelligence, Reinforcement learning, Multi-agent system, Robot and Evolutionary algorithm. As part of his studies on Artificial intelligence, Kagan Tumer often connects relevant areas like Coding. His Reinforcement learning research is under the purview of Machine learning.

In general Machine learning study, his work on Neuroevolution often relates to the realm of Isolation, thereby connecting several areas of interest. His research in Multi-agent system focuses on subjects like Mathematical optimization, which are connected to Credit assignment, Computational intelligence and Sensitivity. As a member of one scientific family, Kagan Tumer mostly works in the field of Robot, focusing on Control and, on occasion, Data collection, Operations research, Autonomous robot and Human-in-the-loop.

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.

Top Publications

Error Correlation and Error Reduction in Ensemble Classifiers

Kagan Tumer;Joydeep Ghosh.
Connection Science (1996)

784 Citations

Analysis of decision boundaries in linearly combined neural classifiers

Kagan Tumer;Joydeep Ghosh.
Pattern Recognition (1996)

434 Citations

Classifier ensembles: Select real-world applications

Nikunj C. Oza;Kagan Tumer.
Information Fusion (2008)

347 Citations

AN INTRODUCTION TO COLLECTIVE INTELLIGENCE

David H. Wolpert;Kagan Tumer.
arXiv: Learning (1999)

277 Citations

Optimal Payoff Functions for Members of Collectives

David H. Wolpert;Kagan Tumer.
Modeling Complexity in Economic and Social Systems. Edited by SCHWEITZER FRANK. Published by World Scientific Publishing Co. Pte. Ltd (2002)

258 Citations

Distributed agent-based air traffic flow management

Kagan Tumer;Adrian Agogino.
adaptive agents and multi-agents systems (2007)

229 Citations

Linear and Order Statistics Combiners for Pattern Classification

Kagan Tumer;Joydeep Ghosh;Sonie Lau.
arXiv: Neural and Evolutionary Computing (1999)

214 Citations

Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks

Kagan Tumer;Nirmala Ramanujam;Rebecca R. Richards-Kortum;Joydeep Ghosh.
neural information processing systems (1996)

167 Citations

General principles of learning-based multi-agent systems

David H. Wolpert;Kevin R. Wheeler;Kagan Tumer.
adaptive agents and multi-agents systems (1999)

132 Citations

Collective intelligence for control of distributed dynamical systems

David H. Wolpert;Kevin R. Wheeler;Kagan Tumer.
EPL (2000)

128 Citations

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

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