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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 2007 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 644 pages, kõrgus x laius: 235x155 mm, kaal: 1015 g, XXIV, 644 p., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 4702
  • Ilmumisaeg: 31-Aug-2007
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540749756
  • ISBN-13: 9783540749752
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  • Formaat: Paperback / softback, 644 pages, kõrgus x laius: 235x155 mm, kaal: 1015 g, XXIV, 644 p., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 4702
  • Ilmumisaeg: 31-Aug-2007
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540749756
  • ISBN-13: 9783540749752
Teised raamatud teemal:
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 1721 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 years 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.
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.