Petri Koistinen

16 papers receiving 565 citations

Hit Papers

Using additive noise in back-propagation training1992202620032014199250100150200250

Peers

Petri Koistinen
Comparison fields: 5 of 112
  • Artificial Intelligence 236
  • Radiology, Nuclear Medicine and Imaging 133
  • Computer Vision and Pattern Recognition 132
  • Biomedical Engineering 122
  • Signal Processing 58
Replace Giuseppe Patanè with:
Giuseppe Patanè Italy
Xiaoheng Tan China
S. Z. Li China
Sebastian Kurtek United States
Andrés Romero France
Jeff Orchard Canada
Jianwei Zheng China
Volodymyr Ponomaryov Mexico
Petri Koistinen relative to Giuseppe Patanè Italy Giuseppe Patanè's profile →
Citations per field
00.5×3.4×
Giuseppe Patanè · 1×
Citations per year

Countries citing papers authored by Petri Koistinen

Since Specialization
Citations

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

Fields of papers citing papers by Petri Koistinen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Petri Koistinen. 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 Petri Koistinen. The network helps show where Petri Koistinen may publish in the future.

Co-authorship network of co-authors of Petri Koistinen

This figure shows the co-authorship network connecting the top 25 collaborators of Petri Koistinen. A scholar is included among the top collaborators of Petri Koistinen 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 Petri Koistinen. Petri Koistinen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
#WorkIndexed citations
1 12
2 24
3 10
4 8
5 67
6 105
7
Electromagnetic Scattering Model for Forest Remote Sensing
3
8
Using smoothing to reconstruct the Holocene temperature in Lapland
2
9
Asymptotic Theory for Regularization: One-Dimensional Linear Case
2
10 68
11
Comparison of Neural and Statistical Classifiers - Theory and Practice
7
12 3
13
Using additive noise in back-propagation trainingbreakdown →
250
14
Kernel Regression and Backpropagation Training With Noise
20
15 25
16 5

About Petri Koistinen

Petri Koistinen is a scholar working on Statistics and Probability, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 16 papers that have together received 611 indexed citations. Recurring topics across this work include Neural Networks and Applications (6 papers), Face and Expression Recognition (3 papers) and Medical Imaging Techniques and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (236 citations), Computer Vision and Pattern Recognition (132 citations) and Radiology, Nuclear Medicine and Imaging (133 citations). Petri Koistinen has collaborated with scholars based in Finland, Germany and United States. Frequent co-authors include Lasse Holmström, Ville Kolehmainen, Erkki Somersalo, Jari P. Kaipio, Matti Lassas, Samuli Siltanen, Erkki Oja, Jorma Laaksonen, Mikko J. Sillanpää and Boby Mathew. Their work appears in journals such as Remote Sensing of Environment, Theoretical and Applied Genetics and Physics in Medicine and 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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2026