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Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings 2014 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 364 pages, kõrgus x laius: 235x155 mm, kaal: 5854 g, 111 Illustrations, black and white; XXII, 364 p. 111 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 8777
  • Ilmumisaeg: 09-Sep-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319118110
  • ISBN-13: 9783319118116
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Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings 2014 ed.Suurem pilt
  • Formaat: Paperback / softback, 364 pages, kõrgus x laius: 235x155 mm, kaal: 5854 g, 111 Illustrations, black and white; XXII, 364 p. 111 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 8777
  • Ilmumisaeg: 09-Sep-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319118110
  • ISBN-13: 9783319118116
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319118116.html
Märksõnad:
This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.
Explaining Mixture Models through Semantic Pattern Mining and Banded
Matrix Visualization.- Big Data Analysis of StockTwits to Predict Sentiments
in the Stock Market.- Synthetic Sequence Generator for Recommender Systems
Memory Biased Random Walk on a Sequence Multilayer Network.- Predicting
Sepsis Severity from Limited Temporal Observations.- Completion Time and Next
Activity Prediction of Processes Using Sequential Pattern Mining.-
Antipattern Discovery in Ethiopian Bagana Songs.- Categorize, Cluster, and
Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics
Platform of the Human Brain Project.- Multilayer Clustering: A Discovery
Experiment on Country Level Trading Data.- Medical Document Mining Combining
Image Exploration and Text Characterization.- Mining Cohesive Itemsets in
Graphs.- Mining Rank Data.- Link Prediction on the Semantic MEDLINE Network:
An Approach to Literature-Based Discovery.- Medical Image Retrieval Using
Multimodal Data.- Fast Computation of the Tree Edit Distance between
Unordered Trees Using IP Solvers.- Probabilistic Active Learning: Towards
Combining Versatility, Optimality and Efficiency.- Incremental Learning with
Social Media Data to Predict Near Real-Time Events.- Stacking Label Features
for Learning Multilabel Rules.- Selective Forgetting for Incremental Matrix
Factorization in Recommender Systems.- Providing Concise Database Covers
Instantly by Recursive Tile Sampling.- Resampling-Based Framework for
Estimating Node Centrality of Large Social Network.- Detecting Maximum k-Plex
with Iterative Proper l-Plex Search.- Exploiting Bhattacharyya Similarity
Measure to Diminish User Cold-Start Problem in Sparse Data.- Failure
Prediction An Application in the Railway Industry.- Wind Power Forecasting
Using Time Series Cluster Analysis.- Feature Selection in Hierarchical
Feature Spaces.- Incorporating Regime Metrics into Latent Variable Dynamic
Models to Detect Early-Warning Signals of Functional Changes inFisheries
Ecology.- An Efficient Algorithm for Enumerating Chordless Cycles and
Chordless Paths.- Algorithm Selection on Data Streams.- Sparse Coding for Key
Node Selection over Networks.- Variational Dependent Multi-output Gaussian
Process Dynamical Systems.