Новости РОС

The World Congress on Engineering and Computer Science (WCECS10)

В рамках Конгресса пройдет International Conference on Machine Learning and Data Analysis (ICMLDA'10).

The topics of the ICMLDA'10 include, but not limited to, the following:

Machine Learning:

• artificial neural networks
• Bayesian networks
• case-based reasoning
• computational models of human learning
• computational learning theory
• cooperative learning
• decision tree
• discovery of scientific laws
• evolutionary computation
• statistical relational learning
• grammatical inference
• incremental induction and on-line learning
• inductive logic programming
• information retrieval and learning
• instance based learning
• kernel methods
• knowledge acquisition and learning
• knowledge base refinement
• knowledge intensive learning
• learning from text and web
• evaluation metrics and methodologies
• machine learning of natural language
• meta learning
• multi-agent learning
• multi-strategy learning
• planning and learning
• reinforcement learning
• revision and restructuring
• statistical approaches
• unsupervised learning
• vision and learning

Data analysis and databases:

• database integration
• inductive databases
• data mining query languages
• data mining query optimization

Foundations of data analysis:

• complexity issues
• knowledge (pattern) representation
• global vs. local patterns
• logic for data mining
• statistical inference and probabilistic modelling
   

Data pre-processing:

• dimensionality reduction
• data reduction
• discretization
• uncertain and missing information handling

Innovative applications:

• web content, structure and usage mining
• semantic web mining
• mining governmental data, mining for the public administration
• personalization
• adaptive data mining architectures
• invisible data mining

Algorithms and techniques:

• classification
• clustering
• frequent patterns
• rule discovery
• statistical techniques and mixture models
• constraint-based mining
• incremental algorithms
• scalable algorithms
• distributed and parallel algorithms
• privacy preserving data mining
• multi-relational data mining

KDD process and process-centric data analysis:

• models of the KDD process
• standards for the KDD process
• background knowledge integration
• collaborative data mining
• vertical data mining environments

Analysis of different forms of data:

• graph, tree, sequence mining
• semi-structured and XML data mining
• text mining
• temporal, spatial, and spatio-temporal data mining
• data stream mining
• multimedia miningPattern post-processing
• Pattern post-processing
• quality assessment
• visualization
• knowledge interpretation and use

Подробности на сайте Конгресса

Организаторы приглашают российских социологов принять участие в Конгрессе.

 

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