Advances in intelligent data analysis : reasoning about data : second international symposium, IDA-97, London, UK, August 4-6, 1997 : proceedings
This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997. The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: I...
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| مؤلف مشترك: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| سلاسل: | Lecture notes in computer science
1280 |
| الموضوعات: | |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Advances in intelligent data analysis, reasoning about data, second international symposium, IDA-97, London, UK, August 1-4, 1997, proceedings, Xiaohui Liu, Paul Cohen, Michael Berthold (eds.), 1997, Berlin, Springer, 1 vol. (XII-620 p.), Lecture notes in computer science, 3-540-63346-4 • Advances in Intelligent Data Analysis. Reasoning about Data, Texte imprimé, 9783662198070 |
جدول المحتويات:
- Intelligent data analysis: Issues and opportunities
- Decomposition of heterogeneous classification problems
- Managing dialogue in a statistical expert assistant with a cluster-based user model
- How to find big-oh in your data set (and how not to)
- Data classification using a W.I.S.E. toolbox
- Mill's methods for complete Intelligent Data Analysis
- Integrating many techniques for discovering structure in data
- Meta-Reasoning for Data Analysis Tool Allocation
- Navigation for data analysis systems
- An annotated data collection system to support intelligent analysis of Intensive Care Unit data
- A combined approach to uncertain data analysis
- A connectionist approach to the distance-based analysis of relational data
- Efficient GA based techniques for automating the design of classification models
- Data representations and machine learning techniques
- Development of a knowledge-driven constructive induction mechanism
- Oblique linear tree
- Feature selection for neural networks through functional links found by evolutionary computation
- Building simple models: A case study with decision trees
- Exploiting symbolic learning in visual inspection
- Forming categories in exploratory data analysis and data mining
- A systematic description of greedy optimisation algorithms for cost sensitive generalisation
- Dissimilarity measure for collections of objects and values
- ECG segmentation using time-warping
- Interpreting longitudinal data through temporal abstractions: An application to diabetic patients monitoring
- Intelligent support for multidimensional data analysis in environmental epidemiology
- Network performance assessment for Neurofuzzy data modelling
- A genetic approach to fuzzy clustering with a validity measure fitness function
- The analysis of artificial neural network data models
- Simulation data analysis using Fuzzy Graphs
- Mathematical analysis of fuzzy classifiers
- Neuro-fuzzy diagnosis system with a rated diagnosis reliability and visual data analysis
- Genetic Fuzzy Clustering by means of discovering membership functions
- A strategy for increasing the efficiency of rule discovery in data mining
- Intelligent text analysis for dynamically maintaining and updating domain knowledge bases
- Knowledge discovery in endgame databases
- Parallel induction algorithms for data mining
- Data analysis for query processing
- Datum discovery
- A connectionist approach to extracting knowledge from databases
- A modulated Parzen-windows approach for probability density estimation
- Improvement on estimating quantites in finite population using indirect methods of estimation
- Robustness of clustering under outliers
- The BANG-clustering system: Grid-based data analysis
- Techniques for dealing with missing values in classification
- The use of exogenous knowledge to learn Bayesian Networks from incomplete databases
- Reasoning about outliers by modelling noisy data
- Reasoning about sensor data for automated system identification
- Modelling discrete event sequences as state transition diagrams
- Detecting and describing patterns in time-varying data using wavelets
- Diagnosis of tank ballast systems
- Qualitative uncertainty models from random set theory.