Nantilus
New Frontiers in Mining Complex Patterns : First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Rivesed Selected Papers
This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012. The 15 revised full papers were carefully reviewed and selecte...
Enregistré dans:
| Auteurs principaux : | , , , , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | New Frontiers in Mining Complex Patterns : First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Rivesed Selected Papers / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras. |
| Publié : |
Berlin, Heidelberg :
Springer Berlin Heidelberg
, 2013 Cham : Springer Nature |
| Collection : | Lecture Notes in Artificial Intelligence ; 7765 |
| Accès en ligne : |
Accès Nantes Université
Accès direct soit depuis les campus via le réseau ou le wifi eduroam soit à distance avec un compte @etu.univ-nantes.fr ou @univ-nantes.fr |
| Note sur l'URL : | Accès sur la plateforme de l'éditeur (Springer) Accès sur la plateforme Istex |
| Condition d'utilisation et de reproduction : | Conditions particulières de réutilisation pour les bénéficiaires des licences nationales : chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |
| Sujets : | |
| Documents associés : | Autre format:
New Frontiers in Mining Complex Patterns Autre format: New Frontiers in Mining Complex Patterns |
- Learning with Configurable Operators and RL-Based Heuristics.- Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks
- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation
- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules
- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets
- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution. Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks
- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation
- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules
- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets
- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-ConstrainedPatterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution. .