会议信息

MLPR 2026: International Conference on Machine Learning and Pattern Recognition

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截稿日期:
2026-06-30 还有 9 天
通知日期:
2026-07-30
会议日期:
2026-12-04
会议地点:
Kyoto, Japan
届数:
4
浏览: 6268   关注: 0   参加: 0

征稿

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 2026-06-30. Acceptance notifications are sent on 2026-07-30.

The conference calls for high-quality, unpublished, original research papers in the theory and practice of machine learning and pattern recognition.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
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CPattern Recognition Letters3.9Elsevier0167-8655
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