Christopher J. Merz

3.2k citations
10 papers · 1.9k indexed · 1 hit paper · h-index 7
Topics
Neural Networks and Applications (7 papers)Machine Learning and Algorithms (2 papers)Face and Expression Recognition (2 papers)
Journals
Machine LearningIEEE ExpertNeural Information Processing Systems
Partner nations
United StatesRussia

In The Last Decade

Christopher J. Merz

9 papers receiving 1.7k citations

Hit Papers

UCI Repository of Machine Learning Databases1996202620062016199650010001.5k

Peers

Christopher J. Merz
Comparison fields: 5 of 95
  • Artificial Intelligence 1.6k
  • Computer Vision and Pattern Recognition 496
  • Information Systems 463
  • Computational Theory and Mathematics 310
  • Signal Processing 232
Replace Patrick M. Murphy with:
Patrick M. Murphy United States
Stephen D. Bay United States
Geoffrey G. Towell United States
Qinbao Song China
Kuhu Pal India
Yuh‐Jye Lee Taiwan
Edwin M. Knorr Canada
Dietrich Wettschereck United States
Ruichu Cai China
Christopher J. Merz relative to Patrick M. Murphy United States Patrick M. Murphy's profile →
Citations per field
00.5×
Patrick M. Murphy · 1×
Citations per year

Countries citing papers authored by Christopher J. Merz

Since Specialization
Citations

This map shows the geographic impact of Christopher J. Merz'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 Christopher J. Merz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher J. Merz more than expected).

Fields of papers citing papers by Christopher J. Merz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher J. Merz. 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 Christopher J. Merz. The network helps show where Christopher J. Merz may publish in the future.

Co-authorship network of co-authors of Christopher J. Merz

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher J. Merz. A scholar is included among the top collaborators of Christopher J. Merz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christopher J. Merz. Christopher J. Merz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
#WorkIndexed citations
1 20
2 0
3 134
4 77
5
Classification and regression by combining models
31
6
Combining Classifiers Using Correspondence Analysis
12
7
Combining Neural Network Regression Estimates with Regularized Linear Weights
32
8
UCI Repository of Machine Learning Databasesbreakdown →
1582
9 1
10 6

About Christopher J. Merz

Christopher J. Merz is a scholar working on Software, Artificial Intelligence and Signal Processing, having authored 10 papers that have together received 1.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Machine Learning and Algorithms (2 papers) and Face and Expression Recognition (2 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Computer Vision and Pattern Recognition (496 citations) and Signal Processing (232 citations). Christopher J. Merz has collaborated with scholars based in United States and Russia. Frequent co-authors include Michael J. Pazzani, Stephen Aylward and Andrea Danyluk. Their work appears in journals such as Machine Learning, IEEE Expert and Neural Information Processing Systems.

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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