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Discovery Science

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Cover of 'Discovery Science'

Table of Contents

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    Book Overview
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    Chapter 1 Mixture Models from Multiresolution 0-1 Data
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    Chapter 2 Model Tree Ensembles for Modeling Dynamic Systems
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    Chapter 3 Fast and Scalable Image Retrieval Using Predictive Clustering Trees
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    Chapter 4 Avoiding Anomalies in Data Stream Learning
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    Chapter 5 Generalizing from Example Clusters
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    Chapter 6 Clustering Based Active Learning for Evolving Data Streams
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    Chapter 7 Robust Crowd Labeling Using Little Expertise
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    Chapter 8 A New Approach to String Pattern Mining with Approximate Match
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    Chapter 9 OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process
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    Chapter 10 A Wordification Approach to Relational Data Mining
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    Chapter 11 Multi-interval Discretization of Continuous Attributes for Label Ranking
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    Chapter 12 Identifying Super-Mediators of Information Diffusion in Social Networks
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    Chapter 13 SM2D: A Modular Knowledge Discovery Approach Applied to Hydrological Forecasting
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    Chapter 14 A Dynamic Programming Algorithm for Learning Chain Event Graphs
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    Chapter 15 Mining Interesting Patterns in Multi-relational Data with N-ary Relationships
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    Chapter 16 Learning Hierarchical Multi-label Classification Trees from Network Data
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    Chapter 17 A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications
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    Chapter 18 Inductive Process Modeling of Rab5-Rab7 Conversion in Endocytosis
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    Chapter 19 Fast Compression of Large-Scale Hypergraphs for Solving Combinatorial Problems
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    Chapter 20 Semantic Data Mining of Financial News Articles
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    Chapter 21 Polynomial Delay and Space Discovery of Connected and Acyclic Sub-hypergraphs in a Hypergraph
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    Chapter 22 Hyperlink Prediction in Hypernetworks Using Latent Social Features
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    Chapter 23 Extracting Opinionated (Sub)Features from a Stream of Product Reviews
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Discovery Science
Published by
Springer Berlin Heidelberg, September 2013
DOI 10.1007/978-3-642-40897-7
978-3-64-240896-0, 978-3-64-240897-7

Fürnkranz, Johannes, Hüllermeier, Eyke, Higuchi, Tomoyuki

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 33%
Student > Bachelor 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Unknown 1 33%