The International Conference on Pattern Recognition Applications and Methods would like to become a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition, 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 these 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
Exact and Approximate Inference
Density Estimation
Bayesian Models
Gaussian Processes
Model Selection
Graphical and Graph-based Models
Missing Data
Ensemble Methods
Neural Networks
Kernel Methods
Large Margin Methods
Classification
Regression
Sparsity
Feature Selection and Extraction
Spectral Methods
Embedding and Manifold Learning
Similarity and Distance Learning
Matrix Factorization
Clustering
ICA, PCA, CCA and other Linear Models
Fuzzy Logic
Active Learning
Cost-sensitive Learning
Incremental Learning
On-line Learning
Structured Learning
Multi-agent Learning
Multi-instance Learning
Reinforcement Learning
Instance-based Learning
Knowledge Acquisition and Representation
Meta Learning
Multi-strategy Learning
Case-Based Reasoning
Inductive Learning
Computational Learning Theory
Cooperative Learning
Evolutionary Computation
Information Retrieval and Learning
Hybrid Learning Algorithms
Planning and Learning
Convex Optimization
Stochastic Methods
Combinatorial Optimization
Multiclassifier Fusion
AREA 2: APPLICATIONS
Natural Language Processing
Information Retrieval
Ranking
Web Applications
Economics, Business and Forecasting Applications
Bioinformatics and Systems Biology
Audio and Speech Processing
Signal Processing
Image Understanding
Sensors and Early Vision
Motion and Tracking
Image-based Modelling
Shape Representation
Object Recognition
Video Analysis
Medical Imaging
Learning and Adaptive Control
Perception
Learning in Process Automation
Learning of Action Patterns
Virtual Environments
Robotics
Biometrics