Dimitri P. Solomatine
Artificial Intelligence · 2001–2017
A researcher in the Science corpus: 10 papers; trusts 28 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 34 citations into it; #11,232 of 35,345 by trust here, #10,566 by citations
- Computational Theory and Mathematics — 23 citations into it; #11,895 of 12,182 by trust here, #2,851 by citations
- Global and Planetary Change — 20 citations into it; #634 of 10,225 by trust here, #3,398 by citations
- Water Science and Technology — 20 citations into it; #41 of 1,711 by trust here, #371 by citations
- Environmental Engineering — 19 citations into it; #263 of 5,479 by trust here, #747 by citations
- Statistics and Probability — 14 citations into it
- Atmospheric Science — 11 citations into it
- Civil and Structural Engineering — 9 citations into it; #3,306 of 6,160 by trust here, #1,641 by citations
Whom Dimitri P. Solomatine trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Ross Quinlan
- Gavin J. Bowden
- Mehmet Emre Cek
- Orazio Giustolisi
- Shie-Yui Liong
- Yonas B. Dibike
- Yunpeng Xue
- Shreedhar Maskey
- Leo Breiman
- Christopher G. Atkeson
Who trusts Dimitri P. Solomatine overall
- C. Wu
- Kwok-Wing Chau
- A. Rahimikhoob
- Krishnan P. Singh
- Wen Wang
- Octavio Loyola-González
- Ozgur Kisi
- Mahesh Pal
- Azal Ahmad Khan
- A. Petersen‐Øverleir
Papers from Dimitri P. Solomatine's perspective
Ordered by how strongly the researchers Dimitri P. Solomatine trusts endorse each paper. Same corpus, a different order for every researcher.
- The Nature of Statistical Learning Theory
- A decision-theoretic generalization of on-line learning and an application to boosting
- Statistical learning theory
- Genetic Algorithms in Search Optimization and Machine Learning
- Algorithms for Clustering Data
- UCI Repository of machine learning databases
- Adaptive Mixtures of Local Experts
- Locally Weighted Learning
- Bagging Predictors
- ImageNet classification with deep convolutional neural networks
Their papers
- Model Induction with Support Vector Machines: Introduction and Applications (2001) — 592 citations
- Can assimilation of crowdsourced data in hydrological modelling improve flood prediction (2017) — 90 citations
- Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014) — 58 citations
- Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014) — 596 citations
- Data-driven modelling: some past experiences and new approaches (2008) — 644 citations
- Machine learning approaches for estimation of prediction interval for the model output (2006) — 425 citations
- Eager and Lazy Learning Methods in the Context of Hydrologic Forecasting (2006) — 8 citations
- Neural networks and M5 model trees in modelling water level-discharge relationship (2005) — 312 citations
- M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China (2004) — 315 citations
- AdaBoost.RT: a boosting algorithm for regression problems (2004) — 319 citations