Open Science Research Excellence

ICPRML 2021 : International Conference on Pattern Recognition and Machine Learning

Madrid, Spain
March 25 - 26, 2021

Call for Papers

ICPRML 2021 : International Conference on Pattern Recognition and Machine Learning is the premier interdisciplinary platform for the presentation of new advances and research results in the fields of Pattern Recognition and Machine Learning. The conference will bring together leading academic scientists, researchers and scholars in the domain of interest from around the world. Topics of interest for submission include, but are not limited to:
  • Pattern Recognition and Basic Technologies
  • Statistical Pattern Recognition
  • Structural and Syntactic Pattern Recognition
  • Neural Networks
  • Machine Learning and Data Mining
  • Artificial Intelligence and Symbolic Learning
  • Classification and Clustering
  • Feature Selection, Dimensionality Reduction, Manifold Learning
  • Kernel Methods and Support Vector Machines
  • Invariance in Recognition
  • Multiresolution Techniques
  • OCR, Document Analysis and Understanding
  • Information Retrieval
  • Pattern Recognition and Computer Vision
  • Sensors and Early Vision
  • Color and Texture
  • Segmentation and Grouping
  • Motion and Tracking
  • Stereo and Structure from Motion
  • Image-Based Modeling
  • Illumination and Reflectance Modeling
  • Shape Representation
  • Object Recognition
  • Video Analysis and Event Recognition
  • Face and Gesture
  • Statistical Methods and Learning
  • Performance Evaluation
  • Medical Image Analysis
  • Image and Video Retrieval
  • Applications
  • Machine Learning Methods and Technologies
  • Artificial neural networks
  • Bayesian networks
  • Case-based reasoning
  • Clustering
  • Computational models of human learning
  • Computational learning theory
  • Cooperative learning
  • Decision tree learning
  • Discovery
  • Ensemble methods
  • Inductive logic programming
  • Information retrieval and learning
  • Instance based learning
  • Kernel methods
  • Knowledge base refinement
  • Knowledge intensive learning
  • Machine learning of natural language
  • Meta learning
  • Multi-agent learning
  • Multi-strategy learning
  • Planning and learning
  • Prediction of complex structures
  • Regression
  • Reinforcement learning
  • Rule learning
  • Statistical approaches
  • Semi-supervised learning
  • Unsupervised learning
  • Vision and learning
  • Computer Vision and Image Analysis
  • Active Vision
  • Early Vision
  • Feature Extraction
  • Motion Analysis
  • Representation
  • Recognition (2D and 3D)
  • Texture and Colour
  • Scene Understanding
  • Segmentation