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Knowledge Discovery in Databases: PKDD 2007: 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings de Joost N. Kok

Knowledge Discovery in Databases: PKDD 2007: 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings: Lecture Notes in Computer Science, cartea 4702

Editat de Joost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic
en Limba Engleză Paperback – 31 aug 2007
The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.AjointProgramCommittee(PC)wasnominatedforthe two conferences, consisting of some 300 renowned researchers, mostly proposed by the Area Chairs. This joint PC, the largest of the series to date, allowed us to exploit synergies and deal competently with topic overlaps between ECML and PKDD. ECML/PKDD 2007 received 592 abstract submissions. As in previous years, toassistthereviewersandtheAreaChairsintheir?nalrecommendationauthors had the opportunity to communicate their feedback after the reviewing phase.
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Specificații

ISBN-13: 9783540749752
ISBN-10: 3540749756
Pagini: 670
Ilustrații: XXIV, 644 p.
Dimensiuni: 155 x 235 x 29 mm
Greutate: 0.93 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Invited Talks.- Learning, Information Extraction and the Web.- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation.- Mining Queries.- Adventures in Personalized Information Access.- Long Papers.- Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning.- Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction.- A Graphical Model for Content Based Image Suggestion and Feature Selection.- Efficient AUC Optimization for Classification.- Finding Transport Proteins in a General Protein Database.- Classification of Web Documents Using a Graph-Based Model and Structural Patterns.- Context-Specific Independence Mixture Modelling for Protein Families.- An Algorithm to Find Overlapping Community Structure in Networks.- Privacy Preserving Market Basket Data Analysis.- Feature Extraction from Sensor Data Streams for Real-Time Human Behaviour Recognition.- Generating Social Network Features for Link-Based Classification.- An Empirical Comparison of Exact Nearest Neighbour Algorithms.- Site-Independent Template-Block Detection.- Statistical Model for Rough Set Approach to Multicriteria Classification.- Classification of Anti-learnable Biological and Synthetic Data.- Improved Algorithms for Univariate Discretization of Continuous Features.- Efficient Weight Learning for Markov Logic Networks.- Classification in Very High Dimensional Problems with Handfuls of Examples.- Domain Adaptation of Conditional Probability Models Via Feature Subsetting.- Learning to Detect Adverse Traffic Events from Noisily Labeled Data.- IKNN: Informative K-Nearest Neighbor Pattern Classification.- Finding Outlying Items in Sets of Partial Rankings.- Speeding Up Feature Subset Selection Through Mutual Information Relevance Filtering.- A Comparison of Two Approaches to Classify with Guaranteed Performance.- Towards Data Mining Without Information on Knowledge Structure.- Relaxation Labeling for Selecting and Exploiting Efficiently Non-local Dependencies in Sequence Labeling.- Bridged Refinement for Transfer Learning.- A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3.- Short Papers.- Flexible Grid-Based Clustering.- Polyp Detection in Endoscopic Video Using SVMs.- A Density-Biased Sampling Technique to Improve Cluster Representativeness.- Expectation Propagation for Rating Players in Sports Competitions.- Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract).- Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach.- Realistic Synthetic Data for Testing Association Rule Mining Algorithms for Market Basket Databases.- Learning Multi-dimensional Functions: Gas Turbine Engine Modeling.- Constructing High Dimensional Feature Space for Time Series Classification.- A Dynamic Clustering Algorithm for Mobile Objects.- A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions.- MINI: Mining Informative Non-redundant Itemsets.- Stream-Based Electricity Load Forecast.- Automatic Hidden Web Database Classification.- Pruning Relations for Substructure Discovery of Multi-relational Databases.- The Most Reliable Subgraph Problem.- Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains.- Searching for Better Randomized Response Schemes for Privacy-Preserving Data Mining.- Pre-processing Large Spatial Data Sets with Bayesian Methods.- Tag Recommendations in Folksonomies.- Providing Naïve Bayesian Classifier-Based Private Recommendations on Partitioned Data.- Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework.- Multilevel Conditional Fuzzy C-Means Clustering of XML Documents.- Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder.- Real Time GPU-Based Fuzzy ART Skin Recognition.- A Cooperative Game Theoretic Approach to Prototype Selection.- Dynamic BayesianNetworks for Real-Time Classification of Seismic Signals.- Robust Visual Mining of Data with Error Information.- An Effective Approach to Enhance Centroid Classifier for Text Categorization.- Automatic Categorization of Human-Coded and Evolved CoreWar Warriors.- Utility-Based Regression.- Multi-label Lazy Associative Classification.- Visual Exploration of Genomic Data.- Association Mining in Large Databases: A Re-examination of Its Measures.- Semantic Text Classification of Emergent Disease Reports.