Vor Ort
Merkzettel Warenkorb
  • Produktbild: Knowledge Discovery in Databases: PKDD 2003
  • Produktbild: Knowledge Discovery in Databases: PKDD 2003

Knowledge Discovery in Databases: PKDD 2003 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings

Aus der Reihe Lecture Notes in Computer Science

99,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.09.2003

Herausgeber

Nada Lavrač + weitere

Verlag

Springer Berlin

Seitenzahl

512

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

791 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-20085-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.09.2003

Herausgeber

Verlag

Springer Berlin

Seitenzahl

512

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

791 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-20085-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Knowledge Discovery in Databases: PKDD 2003
  • Produktbild: Knowledge Discovery in Databases: PKDD 2003
  • Invited Papers.- From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge.- Two-Eyed Algorithms and Problems.- Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data.- Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions.- Contributed Papers.- Efficient Statistical Pruning of Association Rules.- Majority Classification by Means of Association Rules.- Adaptive Constraint Pushing in Frequent Pattern Mining.- ExAnte: Anticipated Data Reduction in Constrained Pattern Mining.- Minimal k-Free Representations of Frequent Sets.- Discovering Unbounded Episodes in Sequential Data.- Mr-SBC: A Multi-relational Naïve Bayes Classifier.- SMOTEBoost: Improving Prediction of the Minority Class in Boosting.- Using Belief Networks and Fisher Kernels for Structured Document Classification.- A Skeleton-Based Approach to Learning Bayesian Networks from Data.- On Decision Boundaries of Naïve Bayes in Continuous Domains.- Application of Inductive Logic Programming to Structure-Based Drug Design.- Visualizing Class Probability Estimators.- Automated Detection of Epidemics from the Usage Logs of a Physicians’ Reference Database.- An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations.- Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications.- Explaining Text Clustering Results Using Semantic Structures.- Analyzing Attribute Dependencies.- Ranking Interesting Subspaces for Clustering High Dimensional Data.- Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases.- Using Transduction and Multi-view Learning to Answer Emails.- Exploring Fringe Settings of SVMs for Classification.- Rule Discovery and Probabilistic Modeling for Onomastic Data.- Constraint-Based Mining of Sequential Patterns over Datasets with Consecutive Repetitions.- Symbolic Distance Measurements Based on Characteristic Subspaces.- The Pattern Ordering Problem.- Collaborative Filtering Using Restoration Operators.- Efficient Frequent Query Discovery in Farmer.- Towards Behaviometric Security Systems: Learning to Identify a Typist.- Efficient Density Clustering Method for Spatial Data.- Statistical ?-Partition Clustering over Data Streams.- Enriching Relational Learning with Fuzzy Predicates.- Text Categorisation Using Document Profiling.- A Simple Algorithm for Topic Identification in 0–1 Data.- Bottom-Up Learning of Logic Programs for Information Extraction from Hypertext Documents.- Predicting Outliers.- Mining Rules of Multi-level Diagnostic Procedure from Databases.- Learning Characteristic Rules Relying on Quantified Paths.- Topic Learning from Few Examples.- Arbogodaï, a New Approach for Decision Trees.