Communications and Discoveries from Multidisciplinary Data /
| Autor Corporativo: | |
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| Otros Autores: | , , , , , |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Colección: | Studies in Computational Intelligence,
123 |
| Materias: |
Tabla de Contenidos:
- Thought, Communication, and Actions
- Sharing Representations and Creating Chances through Cognitive Niche Construction. The Role of Affordances and Abduction
- Discovering and Communicating through Multimodal Abduction
- Creative Community Working on Multidisciplinary Data
- Augmented Analytical Exploitation of a Scientific Forum
- Multi-Data Mining for Understanding Leadership Behavior
- Discussion Visualization on a Bulletin Board System
- Design of BBS with Visual Representation for Online Data Analysis
- A Study on Web Clustering with Respect to XiangShan Science Conference
- Discoveries from Data and Application to Business
- A Multilevel Integration Approach for E-Finance Portal Development
- Integrated Design Framework for Embedded GUI System
- A Unified Probabilistic Inference Model for Targeted Marketing
- Computational Methods for Discoveries from Integrated Data - Human-Interactive Annealing for Multilateral Observation
- Human-Interactive Annealing Process with Pictogram for Extracting New Scenarios for Patent Technology
- Pharmaceutical Drug Design Using Dynamic Connectionist Ensemble Networks
- A Framework of Knowledge Management Platform for Middle and Small Business
- Mining Risks from Multidisciplinary Data
- Discovery of Clusters from Proximity Data: An Approach Using Iterative Adjustment of Binary Classifications
- Evaluating Learning Algorithms to Support Human Rule Evaluation with Predicting Interestingness Based on Objective Rule Evaluation Indices
- Risk Mining for Infection Control
- Evaluating the Error Risk of Email Filters Based on ROC Curve Analysis
- Categorized and Integrated Data Mining of Medical Data
- Privacy-Preserving Data Mining for Medical Data: Application of Data Partition Methods.