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 /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Leung, Kwong S. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Chan, Lai-wan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Meng, Helen (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
• Έκδοση: 1st ed. 2000.
• Σειρά: Lecture Notes in Computer Science, 1983
• Θέματα: Data structures (Computer science).; Artificial intelligence.; Information storage and retrieval.; Application software.; Multimedia information systems.; Information technology.; Business-Data processing.; Data Structures and Information Theory.; Artificial Intelligence.; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).; Multimedia Information Systems.; IT in Business.

 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. 3›8, 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. 

 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 Sites with 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-time Web 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-search Engine Based on Internet Search Service -- A Case-Based Transformation from HTML to XML -- Persistent DOM: An architecture for XML repositories in relational databases -- Multimedia Processing -- Automatic News Video Caption Extraction and Recognition -- Advanced Multilevel Successive Elimination Algorithms for Motion Estimation in Video Coding -- News Content Highlight via Fast Caption Text Detection on Compressed Video -- Texture-based Text Location for Video Indexing -- Video Segmentation by Two Measures and Two Thresholds -- Ink Retrieval from Handwritten Documents -- An Off-Line Recognizer for Hand-Written Chinese Characters -- Bayesian Learning for Image Retrieva Using Multiple Features -- A Content-Based Image Retrieval Method Using Third- Order Color Feature Relations -- Hierarchical Discriminant Regression for Incremental and Real-Time Image Classification -- Distinguishing Real and Virtual Edge Intersection in Pairs of Uncalibrated Images -- Stereo Correspondence Using GA-Based Segmentation -- Special Sessions -- How Adaptive Agents in Stock Market Perform in the Presence of Random News: A Genetic Algorithm Approach -- Learning of Virtual Dealers in an Artificial Market: Comparison with Interview Data -- Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming -- Combining Ordinal Financial Predictions with Genetic Programming -- Applying Independent Component Analysis to Factor Model in Finance -- Web-Based Cluster Analysis System for China and Hong Kong's Stock Market -- Arbitrage-Free Asset Pricing in General State Space -- A Tabu Search Based Algorithm for Clustering Categorical Data Sets -- A New Methodology to Compare Algorithms.