Discovery Science de Johannes Fürnkranz

Discovery Science

Johannes Fürnkranz

Eyke Hüllermeier

Tomoyuki Higuchi

en Limba Engleză Paperback – 13 sep 2013

This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

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Cuprins

Mixture Models from Multiresolution 0-1 Data.- Model Tree Ensembles for Modeling Dynamic Systems.- Fast and Scalable Image Retrieval Using Predictive Clustering Trees.- Avoiding Anomalies in Data Stream Learning.- Generalizing from Example Clusters.- Clustering Based Active Learning for Evolving Data Streams.- Robust Crowd Labeling Using Little Expertise.- A New Approach to String Pattern Mining with Approximate Match.- OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process.- A Wordification Approach to Relational Data Mining.- Multi-interval Discretization of Continuous Attributes for Label Ranking.- Identifying Super-Mediators of Information Diffusion in Social Networks.- SM2D: A Modular Knowledge Discovery Approach Applied to Hydrological Forecasting.- A Dynamic Programming Algorithm for Learning Chain Event Graphs.- Mining Interesting Patterns in Multi-relational Data with N-ary Relationships.- Learning Hierarchical Multi-label Classification Trees from Network Data.- A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications.- Inductive Process Modeling of Rab5-Rab7 Conversion in Endocytosis.- Fast Compression of Large-Scale Hypergraphs for Solving Combinatorial Problems.- Semantic Data Mining of Financial News Articles.- Polynomial Delay and Space Discovery of Connected and Acyclic Sub-hypergraphs in a Hypergraph.- Hyperlink Prediction in Hypernetworks Using Latent Social Features.- Extracting Opinionated (Sub)Features from a Stream of Product Reviews.