William Finnoff
Impact in
- Artificial Intelligence top 10%
- Neural Networks and Applications
- Machine Learning and ELM
- Fuzzy Logic and Control Systems
- Evolutionary Algorithms and Applications
- Metaheuristic Optimization Algorithms Research
- Signal Processing top 10%
- Blind Source Separation Techniques
Papers in
-
- Neural Networks and Applications 7
- Machine Learning and ELM 2
- Machine Learning and Data Classification 1
- Finance 2
- Stochastic processes and financial applications 2
- Co-authors
- F. Hergert (5 shared papers)Hans Georg Zimmermann (1 shared paper)Gustavo Deco (2 shared papers)Ralph Neuneier (1 shared paper)Dirk Ormoneit (1 shared paper)Ulrike M. Krämer (1 shared paper)
- Journals
- Neural Computation (2 papers)Stochastic Processes and their Applications (1 paper)The Annals of Applied Probability (1 paper)Probability Theory and Related Fields (1 paper)Neural Networks (1 paper)
- Partner nations
- Germany
In The Last Decade
William Finnoff
12 papers receiving 326 citations
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 251
- Signal Processing 47
- Statistical and Nonlinear Physics 35
- Computer Vision and Pattern Recognition 60
- Control and Systems Engineering 53
Countries citing papers authored by William Finnoff
This map shows the geographic impact of William Finnoff'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 William Finnoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Finnoff more than expected).
Fields of papers citing papers by William Finnoff
This network shows the impact of papers produced by William Finnoff. 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 William Finnoff. The network helps show where William Finnoff may publish in the future.
Co-authors
The 6 scholars most cited alongside William Finnoff, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1993 | 182 | |
| 2 | 1994 | 53 | |
| 3 | 1995 | 44 | |
| 4 | 1994 | 40 | |
| 5 | 1993 | 13 | |
| 6 | 1991 | 7 | |
| 7 | 1994 | 6 | |
| 8 | 2003 | 4 | |
| 9 | 1993 | 3 | |
| 10 | 1992 | 3 | |
| 11 | 1994 | 2 | |
| 12 | Extended Regularization Methods for Nonconvergent Model Selection | 1992 | 2 |
| 13 | 2002 | 0 |
About William Finnoff
William Finnoff is a scholar working on Artificial Intelligence, Finance, Statistical and Nonlinear Physics, Economics and Econometrics and Control and Systems Engineering, having authored 13 papers that have together received 359 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Complex Systems and Time Series Analysis (3 papers), Stochastic processes and financial applications (2 papers), Model Reduction and Neural Networks (2 papers), Machine Learning and ELM (2 papers), Control Systems and Identification (2 papers), Machine Learning and Data Classification (1 paper) and Economic theories and models (1 paper). The work is most often cited by research in Artificial Intelligence (251 citations), Signal Processing (47 citations), Statistical and Nonlinear Physics (35 citations), Computer Vision and Pattern Recognition (60 citations) and Control and Systems Engineering (53 citations). William Finnoff has collaborated with scholars based in Germany. Frequent co-authors include F. Hergert, Hans Georg Zimmermann, Gustavo Deco, Ralph Neuneier, Dirk Ormoneit and Ulrike M. Krämer. Their work appears in journals such as Neural Computation, Stochastic Processes and their Applications, The Annals of Applied Probability, Probability Theory and Related Fields and Neural Networks.
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.