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Topic · Artificial Intelligence

Machine Learning and ELM

Machine Learning and ELM is a research topic within Artificial Intelligence. Science Explorer counts 14k research works in it since 1965. 24.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the theory, applications, and advancements in Extreme Learning Machines (ELM), a machine learning framework based on feedforward neural networks with random hidden nodes. The papers cover topics such as incremental learning, classification, regression, ensemble methods, kernel-based models, and their applications in various domains.

  • Extreme Learning Machine
  • Feedforward Networks
  • Incremental Learning
  • Random Hidden Nodes
  • Classification
  • Regression
  • Neural Networks
  • Online Sequential Learning
  • Ensemble Learning
  • Kernel Methods
Research works
14k
fractional, since 1965
In the world top 10%
3.4k
per year above
Top-10% rate
24.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+119%
the tick is no change

Which countries lead Machine Learning and ELM research?

By volume, China and India publish the most (2.6k and 669 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.6k works
  2. 2 India 669 works
  3. 3 United States 366 works
  4. 4 South Korea 132 works
  5. 5 Japan 103 works
  6. 6 United Kingdom 90 works
  7. 7 ?? 72 works
  8. 8 Indonesia 71 works
  9. 9 Canada 67 works
  10. 10 Germany 67 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 61.1%India: 15.9%United States: 8.7%South Korea: 3.1%6 others listed: 11.2%61%largest
China2,570 · 61.1%India669 · 15.9%United States366 · 8.7%South Korea132 · 3.1%6 others listed470 · 11.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Learning and ELM research?

By volume in 2022–2025, South China University of Technology publishes the most Machine Learning and ELM research, followed by Beijing University of Technology and Northwestern Polytechnical University.

By volume, 2022–2025

  1. 1 South China University of Technology China 38 works
  2. 2 Beijing University of Technology China 35 works
  3. 3 Northwestern Polytechnical University China 34 works
  4. 4 Xidian University China 33 works
  5. 5 Northeastern University China 32 works
  6. 6 University of Electronic Science and Technology of China China 31 works
  7. 7 Central South University China 31 works
  8. 8 Xi'an Jiaotong University China 30 works
  9. 9 Harbin Institute of Technology China 30 works
  10. 10 Tsinghua University China 29 works

Who are the leading researchers in Machine Learning and ELM?

The most-cited researchers publishing on Machine Learning and ELM include Wei Liu, Philip S. Yu and Dacheng Tao.

  1. 1 Wei Liu 9.7k citations
  2. 2 Philip S. Yu 6.6k citations
  3. 3 Dacheng Tao 6.1k citations
  4. 4 Chunhua Shen 5.9k citations
  5. 5 Witold Pedrycz 5.4k citations
  6. 6 Yann LeCun 5.4k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Machine Learning and ELM research done?

The largest centres of Machine Learning and ELM research in 2022–2025 are Beijing (China), Xi'an (China), Nanjing (China) and Shanghai (China).

Largest cities, 2022–2025

  1. 1 Beijing China 450 works
  2. 2 Xi'an China 160 works
  3. 3 Nanjing China 151 works
  4. 4 Shanghai China 144 works
  5. 5 Guangzhou China 129 works
  6. 6 Chennai India 91 works
  7. 7 Wuhan China 90 works
  8. 8 Hangzhou China 84 works
  9. 9 Chengdu China 84 works
  10. 10 Changsha China 81 works
See Machine Learning and ELM on the map

Where is the best place to study Machine Learning and ELM?

Among universities, judged by research, Beijing University of Technology, Northwestern Polytechnical University and Xidian University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 30.69%fractional works in this node (log) →share in the world top 10% →Beijing University of Technology: 35, 28.0%Northwestern Polytechnical University: 34, 39.5%Xidian University: 33, 25.5%South China University of Technology: 38, 32.3%Indian Institute of Technology Indore: 11, 47.8%Northeastern University: 32, 27.3%Nanjing University of Information Science and Technology: 12, 40.7%Siksha O Anusandhan University: 16, 17.8%Central South University: 31, 30.1%Nanjing University of Posts and Telecommunications: 19, 17.9%Northwestern Polytec…South China Universi…Beijing University o…Xidian University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Beijing University of Technology China 74.628.0%9.7×35 +178.7%
2Northwestern Polytechnical University China 72.239.5%5.8×34 +216.0%
3Xidian University China 68.725.5%8.8×33 +156.5%
4South China University of Technology China 68.032.3%7.0×38 +120.4%
5Indian Institute of Technology Indore India 66.847.8%12.4×11 —
6Northeastern University China 63.827.3%7.2×32 +143.2%
7Nanjing University of Information Science and Technology China 61.940.7%6.0×12 +363.5%
8Siksha O Anusandhan University India 60.417.8%15.6×16 +1266.7%
9Central South University China 59.330.1%3.7×31 +471.5%
10Nanjing University of Posts and Telecommunications China 57.817.9%8.5×19 +389.8%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Machine Learning and ELM research growing?

Output in 2018–2022 was 119% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Learning and ELM.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.