Discovery Science [E-Book]: 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings / edited by Jean-Gabriel Ganascia, Philippe Lenca, Jean-Marc Petit.
Ganascia, Jean-Gabriel.
Lenca, Philippe. / Petit, Jean-Marc.
Berlin, Heidelberg : Springer Berlin Heidelberg : 2012
Imprint: Springer,
XIV, 329 p. 77 illus. digital.
englisch
9783642334924
10.1007/978-3-642-33492-4
Lecture notes in computer science ; 7569
Full Text
Table of Contents:
  • Declarative Modeling for Machine Learning and Data Mining
  • Recent Developments in Pattern Mining
  • Exploring Sequential Data
  • Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers
  • Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting
  • Policy Search in a Space of Simple Closed-form Formulas: Towards Interpretability of Reinforcement Learning
  • Towards Finding Relational Redescriptions
  • Descriptive Modeling of Systemic Banking Crises
  • A Trim Distance between Positions in Nucleotide Sequences
  • Data Squashing for HSV Subimages by an Autonomous Mobile Robot
  • Cohesive Co-evolution Patterns in Dynamic Attributed Graphs
  • Efficient Redundancy Reduced Subgroup Discovery via Quadratic Programming
  • HCAC: Semi-supervised Hierarchical Clustering Using Confidence- Based Active Learning
  • LF-CARS: A Loose Fragment-Based Consensus Clustering Algorithm with a Robust Similarity
  • Fast Approximation Algorithm for the 1-Median Problem
  • Online Co-regularized Algorithms
  • Fast Progressive Training of Mixture Models for Model Selection
  • Including Spatial Relations and Scales within Sequential Pattern Extraction
  • Predicting Ramp Events with a Stream-Based HMM Framework
  • Burst Detection in a Sequence of Tweets Based on Information Diffusion Model
  • Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction
  • An Assessment on Loan Performance from Combined Quantitative and Qualitative Data in XML
  • Structural Change Pattern Mining Based on Constrained Maximal k-Plex Search
  • Enhancing Patent Expertise through Automatic Matching with Scientific Papers
  • Soft Threshold Constraints for Pattern Mining.