Cover Image
Read Now

Advances in Intelligent Data Analysis XVIII 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings

This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data A...

Full description

Bibliographic Details
Other Authors: Berthold, Michael R. (Editor), Feelders, Ad (Editor), Krempl, Georg (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2020, 2020
Edition:1st ed. 2020
Series:Information Systems and Applications, incl. Internet/Web, and HCI
Subjects:
Machine Learning
Computers
Database Management
Data Mining
Computer Networks 
Computer Engineering And Networks
Computer Engineering
Data Mining And Knowledge Discovery
Computing Milieux
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 05140nmm a2200445 u 4500
001 EB001896250
003 EBX01000000000000001059256
005 00000000000000.0
007 cr|||||||||||||||||||||
008 200506 ||| eng
020 |a 9783030445843 
100 1 |a Berthold, Michael R.  |e [editor] 
245 0 0 |a Advances in Intelligent Data Analysis XVIII  |h Elektronische Ressource  |b 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings  |c edited by Michael R. Berthold, Ad Feelders, Georg Krempl 
250 |a 1st ed. 2020 
260 |a Cham  |b Springer International Publishing  |c 2020, 2020 
300 |a XIV, 588 p. 210 illus., 132 illus. in color  |b online resource 
505 0 |a LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification -- Angle-Based Crowding Degree Estimation for Many-Objective Optimization 
505 0 |a AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model -- Detection ofDerivative Discontinuities in Observational Data -- Improving Prediction with Causal Probabilistic Variables -- DO-U-Net for Segmentation and Counting -- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media -- Event Recognition Based on Classification of Generated Image Captions -- Human-to-AI Coach: Improving Human Inputs to AI Systems -- Aleatoric and Epistemic Uncertainty with Random Forests -- Master your Metrics with Calibration -- Supervised Phrase-Boundary Embeddings -- Predicting Remaining Useful Life with Similarity-Based Priors -- Orometric Methods in Bounded Metric Data -- Interpretable Neuron Structuring with Graph Spectral Regularization -- Comparing the Preservation of Network Properties by Graph Embeddings -- Making Learners (More) Monotone -- Combining Machine Learning and Simulation to a Hybrid Modelling Approach --  
505 0 |a Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams -- Widening for MDL-Based Retail Signature Discovery -- Addressing the Resolution Limit and the Field of View Limit in Community Mining -- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics -- Adversarial Attacks Hidden in Plain Sight -- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code -- Overlapping Hierarchical Clustering (OHC) -- Digital Footprints of International Migration on Twitter -- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks -- A Late-Fusion Approach to Community Detection in Attributed Networks -- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction -- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization -- Actionable Subgroup Discovery and Urban Farm Optimization --  
505 0 |a Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder -- Dual Sequential Variational Autoencoders for Fraud Detection -- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks -- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams -- GraphMDL: Graph Pattern Selection Based on Minimum Description Length -- Towards Content Sensitivity Analysis -- Gibbs Sampling Subjectively Interesting Tiles -- Even Faster Exact k-Means Clustering -- Ising-Based Consensus Clustering on Special Purpose Hardware -- Transfer Learning by Learning Projections from Target to Source -- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs -- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces -- Vouw: Geometric Pattern Mining Using the MDL Principle -- A Consensus Approach to Improve NMF Document Clustering --  
653 |a Machine learning 
653 |a Machine Learning 
653 |a Computers 
653 |a Database Management 
653 |a Data mining 
653 |a Computer networks  
653 |a Computer Engineering and Networks 
653 |a Computer engineering 
653 |a Data Mining and Knowledge Discovery 
653 |a Database management 
653 |a Computing Milieux 
700 1 |a Feelders, Ad  |e [editor] 
700 1 |a Krempl, Georg  |e [editor] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Information Systems and Applications, incl. Internet/Web, and HCI 
028 5 0 |a 10.1007/978-3-030-44584-3 
856 4 0 |u https://doi.org/10.1007/978-3-030-44584-3?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 005.74 
520 |a This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation 

Similar Items