Conference Information

MLPR 2026: International Conference on Machine Learning and Pattern Recognition

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Submission Date:
2026-08-20 Extended
Notification Date:
2026-09-20
Conference Date:
2026-12-04
Location:
Kyoto, Japan
Years:
4
Organizer:
Viewed: 7041   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

43.5 / 100
Ranked #3,485 of 5,650 conferences · Top 62%

#172 of 241 in Computer Vision & Pattern Recognition #437 of 735 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
43
Community attention (10%)
21
Public record completeness (15%)
35

Inputs used: Editions on record: 4 · Researchers who opened this page in the past 24 months: 16

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-06

Call For Papers

MLPR 2026 (International Conference on Machine Learning and Pattern Recognition) is an academic conference held in Kyoto, Japan on 2026-12-04. The paper submission deadline is 2026-08-20 (extended). Acceptance notifications are sent on 2026-09-20.

The conference calls for high-quality, unpublished, original research papers in the theory and practice of machine learning and pattern recognition. We encourage submissions from all over the world. Topics of interest include but are not limited to: Machine Learning ▪ Active learning ▪ Dimensionality reduction ▪ Feature selection ▪ Graphical models ▪ Imitation learning ▪ Intelligent business computing ▪ Intelligent systems ▪ Intelligent control system ▪ Intelligent human machine interface ▪ Intelligent robot ▪ Latent variable models ▪ Learning for big data ▪ Learning from noisy supervision ▪ Learning in graphs ▪ Multi-objective learning ▪ Multiple instance learning ▪ Multi-task learning ▪ Online learning ▪ Optimization ▪ Reinforcement learning ▪ Relational learning ▪ Semi-supervised learning ▪ Sparse learning ▪ Statistical machine learning ▪ Structured output learning ▪ Supervised learning ▪ Transfer learning ▪ Unsupervised learning ▪ Other machine learning methodologies Pattern Recognition ▪ Analysis and detection of singularities ▪ Animation image analysis ▪ Classification ▪ Cluster analysis ▪ Deformation analysis ▪ Descriptor of shapes ▪ Diagnosis of faults ▪ Document analysis ▪ Emotion computation ▪ Enhancement and restoration ▪ Feature extraction ▪ Hand gestures classification ▪ Human face recognition ▪ Image compression ▪ Image fusion ▪ Image indexing and retrieval ▪ Image recovery ▪ Invariant representation of patterns ▪ Iris pattern recognition ▪ Learning theory ▪ Machine vision ▪ Medical image analysis ▪ Noise reduction ▪ Nonstationary stochastic processing ▪ Range imaging and detection ▪ Segmentation ▪ Stochastic pattern recognition ▪ Texture analysis and classification ▪ Visualization
Last updated by Dunn Carl on

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