Xiaohu Wen

Environmental Engineering · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 20 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 54 citations into it; #5,322 of 5,479 by trust here, #1,728 by citations
• Artificial Intelligence — 40 citations into it; #25,724 of 35,345 by trust here, #21,316 by citations
• Global and Planetary Change — 37 citations into it; #9,795 of 10,225 by trust here, #4,014 by citations
• Water Science and Technology — 30 citations into it; #1,631 of 1,711 by trust here, #623 by citations
• Electrical and Electronic Engineering — 25 citations into it; #9,957 of 16,539 by trust here, #10,154 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Control and Systems Engineering — 6 citations into it
• Scaling & Learning Theory — 6 citations into it

Whom Xiaohu Wen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Chih-Jen Lin
2. Vladimir Naumovich Vapni
3. Ozgur Kisi
4. Ravinesh C. Deo
5. Bernhard E. Boser
6. Zhi-bin He
7. Vahid Nourani
8. C. Wu
9. Kwok-Wing Chau
10. Vladimir Naumovich Vapnik

Who trusts Xiaohu Wen overall

1. Behrooz Keshtegar
2. Zhenliang Yin
3. Ozgur Kisi
4. Ping-Huan Kuo
5. Ravinesh C. Deo
6. Dazhi Yang
7. Qi Feng
8. Zaher Mundher Yaseen
9. Quoc Bao Pham
10. Zhongmin Liang

Papers from Xiaohu Wen's perspective

Ordered by how strongly the researchers Xiaohu Wen trusts endorse each paper. Same corpus, a different order for every researcher.

1. The Nature of Statistical Learning Theory
2. A training algorithm for optimal margin classifiers
3. Gradient-based learning applied to document recognition
4. Support-Vector Networks
5. Neural Networks: A Comprehensive Foundation
6. A wavelet tour of signal processing
7. Model Induction with Support Vector Machines: Introduction and Applications
8. Predicting Time Series with Support Vector Machines
9. ImageNet classification with deep convolutional neural networks
10. Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process

Their papers

• A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset (2016) — 339 citations
• Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018) — 54 citations
• Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area (2017) — 64 citations
• Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions (2015) — 146 citations
• Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015) — 73 citations
• A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region (2014) — 330 citations