Ching-pei Lee

463 citations
13 papers · 269 · h-index 7

Impact in

Papers in

Ching-pei Lee

13 papers receiving 259 citations

Peers

Ching-pei Lee
Comparison fields: 5 of 57
  • Artificial Intelligence 140
  • Signal Processing 37
  • Computer Vision and Pattern Recognition 69
  • Computational Mathematics 2
  • Information Systems 72
Replace Srinivas Vadrevu with:
Srinivas Vadrevu United States
Matthew Simpson United States
Lishan Ke China
Lorie M. Liebrock United States
Qingqing Long China
Xu Bai China
Finnegan Southey Canada
Tian Gao United States
Rainer Hoch Germany
Ching-pei Lee relative to Srinivas Vadrevu United States Srinivas Vadrevu's profile →
Citations per field
00.5×6.3×
Srinivas Vadrevu · 1×
Citations per year

Countries citing papers authored by Ching-pei Lee

Since Specialization
Citations

This map shows the geographic impact of Ching-pei Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ching-pei Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ching-pei Lee more than expected).

Fields of papers citing papers by Ching-pei Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ching-pei Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ching-pei Lee. The network helps show where Ching-pei Lee may publish in the future.

Co-authors

The 25 scholars most cited alongside Ching-pei Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ching-pei Lee Line = papers co-authored together Ching-pei Lee links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 201479
2 201354
3 201447
4 201428
5
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
201523
6
A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012
201218
7 20197
8 20075
9 20233
10
The Common-directions Method for Regularized Empirical Risk Minimization
20192
11
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
20201
12 20221
13
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems
20191

About Ching-pei Lee

Ching-pei Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Statistics and Probability and Numerical Analysis, having authored 13 papers that have together received 269 indexed citations. Recurring topics across this work include Face and Expression Recognition (5 papers), Sparse and Compressive Sensing Techniques (4 papers), Stochastic Gradient Optimization Techniques (3 papers), Neural Networks and Applications (2 papers), Advanced Optimization Algorithms Research (2 papers), Machine Learning and ELM (2 papers), Imbalanced Data Classification Techniques (1 paper) and Advanced Statistical Methods and Models (1 paper). The work is most often cited by research in Artificial Intelligence (140 citations), Signal Processing (37 citations), Computer Vision and Pattern Recognition (69 citations), Computational Mathematics (2 citations) and Information Systems (72 citations). Ching-pei Lee has collaborated with scholars based in Taiwan, United States and Singapore. Frequent co-authors include Chih‐Jen Lin, Dan Roth, Wei‐Lun Huang, He Yang, Stephen J. Wright, Duan Wu, Anthony Gitter, Michael A. Newton, Chun-Sung Ferng and Julie C. Mitchell. Their work appears in journals such as Neural Computation, Mathematical Programming Computation, Journal of Machine Learning Research, Mathematical Programming and PLoS Computational Biology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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