Discovery Science
Springer International Publishing (Verlag)
978-3-319-24281-1 (ISBN)
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.
| Erscheinungsdatum | 08.10.2016 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XV, 342 p. 96 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Active learning • Algorithm analysis and problem complexity • Artificial Intelligence • artificial intelligence (incl. robotics) • Biomedical Knowledge Discovery • Clustering • Computer Science • Database Management • Data Mining • data mining and knowledge discovery • data stream mining • Evolving Data and Models • evolving datastreams • Human Computer Interaction • Information storage and retrieval • Kernel Methods • Knowledge Discovery • Learning from text • machine learning • Mining Graphs and Structures Data • Semi-Supervised Learning • spatio-temporal data • stream mining • SVM • Web mining |
| ISBN-10 | 3-319-24281-4 / 3319242814 |
| ISBN-13 | 978-3-319-24281-1 / 9783319242811 |
| Zustand | Neuware |
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