Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
580
Utgivningsdatum
2000-11-01
Upplaga
2000 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Leung, K. S. (ed.), Chan, L. (ed.), Meng, H. (ed.)
Illustrationer
XVI, 580 p.
Dimensioner
234 x 156 x 31 mm
Vikt
822 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISSN
0302-9743
ISBN
9783540414506

Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents

Second International Conference Shatin, N.T., Hong Kong, China, December 13-15, 2000. Proceedings

Häftad,  Engelska, 2000-11-01
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X Table of Contents Table of Contents XI XII Table of Contents Table of Contents XIII XIV Table of Contents Table of Contents XV XVI Table of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): IDEAL 2000, LNCS 1983, pp. 38, 2000. Springer-Verlag Berlin Heidelberg 2000 4 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 5 6 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 7 0.6 1.5 0.4 1 0.2 0.5 0 0 10 100 1000 10000 10 100 1000 Mutual information (bits) Mutual information (bits) 8 J. Sinkkonen and S. Kaski 20 10 0 0.1 0.3 0.5 0.7 Mutual information (mbits) Analyses on the Generalised Lotto-Type Competitive Learning Andrew Luk St B&P Neural Investments Pty Limited, Australia Abstract, In generalised lotto-type competitive learning algorithm more than one winner exist. The winners are divided into a number of tiers (or divisions), with each tier being rewarded differently. All the losers are penalised (which can be equally or differently). In order to study the various properties of the generalised lotto-type competitive learning, a set of equations, which governs its operations, is formulated. This is then used to analyse the stability and other dynamic properties of the generalised lotto-type competitive learning.
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Innehållsförteckning

Data Mining and Automated Learning.- Clustering by Similarity in an Auxiliary Space.- Analyses on the Generalised Lotto-Type Competitive Learning.- Extended K-means with an Efficient Estimation of the Number of Clusters.- An Interactive Approach to Building Classiffication Models by Clustering and Cluster Validation.- A New Distributed Algorithm for Large Data Clustering.- A NEW NONHIERARCHICAL CLUSTERING PROCEDURE FOR SYMBOLIC OBJECTS.- Quantization of Continuous Input Variables for Binary Classification.- Information-Based Classification by Aggregating Emerging Patterns.- Boosting the Margin Distribution.- Detecting a Compact Decision Tree Based on an Appropriate Abstraction.- A New Algorithm to Select Learning Examples from Learning Data.- Data Ranking Based on Spatial Partitioning.- Logical Decision Rules: Teaching C4.5 to Speak Prolog.- Visualisation of Temporal Interval Association Rules.- Lithofacies Characteristics Discovery from Well Log Data Using Association Rules.- Fuzzy Hydrocyclone Modelling for Particle Separation Using Fuzzy Rule Interpolation.- A Data-Driven Fuzzy Approach to Robot Navigation Among Moving Obstacles.- Best Harmony Learning.- Observational Learning with Modular Networks.- Finding Essential Attributes in Binary Data.- A Heuristic Optimal Reduct Algorithm.- A Note on Learning Automata Based Schemes for Adaptation of BP Parameters.- A Note on Covariances for Categorical Data.- A General Class of Neural Networks for Principal Component Analysis and Factor Analysis.- Generalised Canonical Correlation Analysis.- Integrating KPCA with an Improved Evolutionary Algorithm for Knowledge Discovery in Fault Diagnosis.- Temporal Data Mining Using Multilevel-Local Polynomial Models.- First Experiments for Mining Sequential Patterns on Distributed Siteswith Multi-Agents.- Nonlinear and Noisy Time Series Prediction Using a Hybrid Nonlinear Neural Predictor.- Genetic Programming Prediction of Solar Activity.- Interpretation of the Richardson Plot in Time Series Representation.- Financial Engineering.- Wavelet Methods in PDE Valuation of Financial Derivatives.- Fast Algorithms for Computing Corporate Default Probabilities.- Variance-Penalized Reinforcement Learning for Risk-Averse Asset Allocation.- Applying Mutual Information to Adaptive Mixture Models.- Stability Analysis of Financial Ratios.- Modeling of the German Yield Curve by Error Correction Neural Networks.- Feature Selection for Support Vector Machines in Financial Time Series Forecasting.- ?-Descending Support Vector Machines for Financial Time Series Forecasting.- Classifying Market States with WARS.- Left Shoulder Detection in Korea Composite Stock Price Index Using an Auto-Associative Neural Network.- Intelligent Agents.- A Computational Framework for Convergent Agents.- Multi-agent Integer Programming.- Building an Ontology for Financial Investment.- A Multi-Agent Negotiation Algorithm for Load Balancing in CORBA-Based Environment.- Existence of Minority in Multi-Agent Systems using Voronoi Tessellation.- Combining Exploitation-Based and Exploration-Based Approach in Reinforcement Learning.- A Probabilistic Agent Approach to the Trafic Jam Problem.- Round-Table Architecture for Communication in Multi-agent Softbot Systems.- Mobile Agents for Reliable Migration in Networks.- Internet Applications.- A Construction of the Adapted Ontology Server in EC.- A Design and Implementation of Cyber Banking Process and Settlement System for Internet Commerce.- A Shopping Agent That Automatically Constructs Wrappers for Semi-Structured Online Vendors.- Real-timeWeb Data Mining and Visualisation.- Web Guide: Filtering and Constraining Site Browsing through Web Walker Techniques.- Topic Spotting on News Articles with Topic Repository by Controlled Indexing.- Validating the Behavior of Self-Interested Agents in an Information Market Scenario.- Discovering User Behavior Patterns in Personalized Interface Agents.- An Agent-Based Personalized Search on a Multi-se