Discovery Science (eBook, PDF)

18th International Conference, DS 2015, Banff, AB, Canada, October 4-6, 2015. Proceedings
Redaktion: Japkowicz, Nathalie; Matwin, Stan

• Format: PDF

Produktbeschreibung

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions.

The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 342
• Erscheinungstermin: 4. Oktober 2015
• Englisch
• ISBN-13: 9783319242828
• Artikelnr.: 43909768

Inhaltsangabe

Bilinear Predictionusing Low Rank Models.- Finding Hidden Structure in Data with TensorDecompositions.- Turning Prediction Tools Into Decision Tools.- Overcomingobstacles to the adoption of machine learning by domain Experts.- Resolutiontransfer in cancer classification based on amplification patterns.- VeryShort-Term Wind Speed Forecasting using Spatio-Temporal Lazy Learning.- Discoveryof Parameters for Animation of Midge Swarms.- No Sentiment is an Island:Author's activity and sentiments transactions in sentiment classification.- ActiveLearning for Classifying Template Matches in Historical Maps.- An evaluation ofscore descriptors combined with non-linear models of expressive dynamics inmusic.- Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of EnvironmentalData Analysis.- Generalized Shortest Path Kernel on Graphs.- Ensembles ofextremely randomized trees for multi-target regression.- Clustering-BasedOptimised Probabilistic Active Learning (COPAL).- Predictive Analysis onTracking Emails for Targeted Marketing.- Semi-supervised Learning for StreamRecommender Systems.- Detecting Transmembrane Proteins Using Decision Trees.- Changepoint detection for information diffusion tree.- Multi-label Classification viaMulti-target Regression on Data Streams.- Periodical Skeletonization forPartially Periodic Pattern Mining.- Predicting Drugs Adverse Side-Effects usinga recommender-system.- Dr. Inventor Framework: extracting structuredinformation from scientific publications.- Predicting Protein Function andProtein-Ligand Interaction with the 3D Neighborhood Kernel.- HierarchicalMultidimensional Classification of web documents with MultiWebClass.- Evaluatingthe Effectiveness of Hashtags as Predictors of the Sentiment of Tweets.- On theFeasibility of Discovering Meta-Patterns from a Data Ensemble.- An Algorithmfor Influence Maximization in a Two-Terminal Series.- Parallel Graph and ItsApplication to a Real Network.- Benchmarking Stream Clustering for ChurnDetection in Dynamic Networks .- Canonical Correlation Methods for ExploringMicrobe-Environment Interactions in Deep Subsurface.- KeCo: Kernel-based OnlineCo-agreement Algorithm.- Tree PCA for Extracting Dominant Substructures fromLabeled Rooted Trees.- Enumerating Maximal Clique Sets with Pseudo-CliqueConstraint.

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