Conference Information

ICPRAM 2025: International Conference on Pattern Recognition Applications and Methods

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Submission Date:
2024-10-02
Notification Date:
2024-12-04
Conference Date:
2025-02-23
Location:
Porto, Portugal
Years:
14
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Call For Papers

ICPRAM 2025 (International Conference on Pattern Recognition Applications and Methods) is an academic conference held in Porto, Portugal on 2025-02-23. The paper submission deadline is 2024-10-02. Acceptance notifications are sent on 2024-12-04.

SCOPE The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives. Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged. Papers describing original work are invited in any of the areas listed below. Accepted papers, presented at the conference by one of the authors, will be published in the proceedings of ICPRAM with an ISBN. Acceptance will be based on quality, relevance and originality. There will be both oral and poster sessions. Special sessions, dedicated to case-studies and commercial presentations, as well as technical tutorials, dedicated to technical/scientific topics, are also envisaged: companies interested in presenting their products/methodologies or researchers interested in presenting a demo or lecturing a tutorial are invited to contact the conference secretariat. CONFERENCE AREAS Each of the topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. THEORY AND METHODS 2. APPLICATIONS AREA 1: THEORY AND METHODS Advanced Learning Methods Linear Models and Dimensionality Reduction Machine Learning Methods Model Representation and Selection Shape Representation Signal Processing Bayesian Models and Stochastic Methods Classification and Clustering Deep Learning and Neural Networks Ensemble Methods Feature Selection and Extraction Knowledge Acquisition and Representation AREA 2: APPLICATIONS Audio and Speech Analysis Motion Tracking and Action Recognition Natural Language Processing Robotics Sensors and Early Vision Virtual Environments Bioinformatics and Systems Biology Biometrics Data Mining and Algorithms for Big Data Document Analysis Image and Video Analysis and Understanding Industry Related Applications Information Retrieval Medical Imaging
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