Discovery Science

Declarative Modeling for Machine Learning and Data Mining.- Recent Developments in Pattern Mining.- Exploring Sequential Data.- Large Scale Spectral Clustering Using Resistance Distance andSpielman-Teng Solvers.- Prediction of Quantiles by Statistical Learning and Application toGDP Forecasting.- Policy Search in a Space of Simple Closed-form Formulas: TowardsInterpretability 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 MobileRobot.- Cohesive Co-evolution Patterns in Dynamic Attributed Graphs.-Efficient Redundancy Reduced Subgroup Discovery via QuadraticProgramming.- HCAC: Semi-supervised Hierarchical Clustering Using Confidence-Based Active Learning.- LF-CARS: A Loose Fragment-Based Consensus Clustering Algorithmwith 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 PatternExtraction.- Predicting Ramp Events with a Stream-Based HMM Framework.- Burst Detection in a Sequence of Tweets Based on InformationDiffusion Model.- Error-Correcting Output Codes as a Transformation from Multi-Classto Multi-Label Prediction.- An Assessment on Loan Performance from Combined Quantitative andQualitative Data in XML.- Structural Change Pattern Mining Based on Constrained Maximalk-Plex Search.- Enhancing Patent Expertise through Automatic Matching withScientific Papers.- Soft Threshold Constraints for Pattern Mining.