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ICPRAM 2017 : International Conference on Pattern Recognition Applications and MethodsConference Series : International Conference on Pattern Recognition Applications and Methods | |||||||||||||||
Link: http://www.icpram.org/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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 |
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