KDMiLe 2016 : 4th Symposium on Knowledge Discovery, Mining and Learning

October 9 -11 , 2016

Recife, Pernambuco, Brazil

Call For Papers

The Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences related to the Data Mining and Machine Learning areas. KDMiLe is organized alternately in conjunction with the Brazilian Conference on Intelligent Systems (BRACIS) and the Brazilian Symposium on Databases (SBBD). This year, in its fourth edition, KDMiLe will be held in Recife, state of Pernambuco, Brazil from October 09 to 11 in conjunction with The Brazilian Conference on Intelligent Systems (BRACIS). The KDMiLe Program Committee invites submissions containing new ideas and proposals, and also applications, in the Data Mining and Machine Learning areas. Submitted papers will be reviewed based on originality, relevance, technical soundness and clarity of presentation.

Topics of Interest

Data Mining Topics:
• Association Rules
• Classification
• Clustering
• Data Mining Applications
• Data Mining Foundations
• Evaluation Methodology in Data Mining
• Feature Selection and Dimensionality Reduction
• Graph Mining
• Massive Data Mining
• Multimedia Data Mining
• Multirelational Mining
• Outlier Detection
• Parallel and Distributed Data Mining
• Pre and Post Processing
• Ranking and Preference Mining
• Privacy and Security in Data Mining
• Quality and Interest Metrics
• Recommender Systems based on Data Mining
• Sequential Patterns
• Social Network Mining
• Stream Data Mining
• Text Mining
• Time-Series Analysis
• Visual Data Mining
• Web Mining
Machine Learning Topics:
• Active Learning
• Bayesian Inference
• Case-Based Reasoning
• Cognitive Models of Learning
• Constructive Induction and Theory Revision
• Cost-Sensitive Learning
• Deep Learning
• Ensemble Methods
• Evaluation Methodology in Machine Learning
• Fuzzy Learning Systems
• Inductive Logic Programming and Relational Learning
• Kernel Methods
• Knowledge-Intensive Learning
• Learning Theory
• Machine Learning Applications
• Meta-Learning
• Multi-Agent and Co-Operative Learning
• Natural Language Processing
• Online Learning
• Probabilistic and Statistical Methods
• Ranking and Preference Learning
• Recommender Systems based on Machine Learning
• Reinforcement Learning
• Semi-Supervised Learning
• Supervised Learning
• Unsupervised Learning

IMPORTANT DATES

Submission deadline: August 10, 2016.

Notification Due: September 8, 2016.

                       

User Name : jerish
Posted 22-07-2016 on 14:36:34 AEDT

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