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Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings / edited by Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou.

Publication:
Cham : Springer International Publishing : Imprint: Springer, 2016.
Format/Description:
Book
1 online resource (XIII, 404 pages) : 146 illustrations
Edition:
1st ed. 2016.
Series:
Computer Science (Springer-11645)
LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI SL 3, 9897
Information Systems and Applications, incl. Internet/Web, and HCI ; 9897
Contained In:
Springer eBooks
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Subjects:
Database management.
Application software.
Artificial intelligence.
Information storage and retrieval.
Algorithms.
Data mining.
Local subjects:
Database Management. (search)
Information Systems Applications (incl. Internet). (search)
Artificial Intelligence. (search)
Information Storage and Retrieval. (search)
Algorithm Analysis and Problem Complexity. (search)
Data Mining and Knowledge Discovery. (search)
System Details:
text file PDF
Summary:
This book constitutes the refereed conference proceedings of the 15th International Conference on Intelligent Data Analysis, which was held in October 2016 in Stockholm, Sweden. The 36 revised full papers presented were carefully reviewed and selected from 75 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The symposium aims to provide a forum for inspiring research contributions that might be considered preliminary in other leading conferences and journals, but that have a potentially dramatic impact. .
Contents:
DSCo-NG: A Practical Language Modeling Approach for Time Series Classification
Ranking Accuracy for Logistic-GEE models
The Morality Machine: Tracking Moral Values in Tweets
A Hybrid Approach for Probabilistic Relational Models Structure Learning
On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks
Obtaining Shape Descriptors from a Concave Hull-Based Clustering Algorithm
Visual Perception of Discriminative Landmarks in Classified Time Series
Spotting the Diffusion of New Psychoactive Substances over the Internet
Feature Selection Issues in Long-Term Travel Time Prediction
A Mean-Field Variational Bayesian Approach to Detecting Overlapping Communities with Inner Roles Using Poisson Link Generation
Online Semi-supervised Learning for Multi-target Regression in Data streams Using AMRules
A Toolkit for Analysis of Deep Learning Experiments
The Optimistic Method for Model Estimation
Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML
Learning from the News: Predicting Entity Popularity on Twitter
Multi-scale Kernel PCA and Its Application to Curvelet-based Feature Extraction for Mammographic Mass Characterization
Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text
Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data
Widened Learning of Bayesian Network Classifiers
Vote Buying Detection via Independent Component Analysis
Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining
A Framework for Interpolating Scattered Data Using Space-filling Curves
Privacy-Awareness of Distributed Data Clustering Algorithms Revisited
Bi-stochastic Matrix Approximation Framework for Data Co-clustering
Sequential Cost-Sensitive Feature Acquisition
Explainable and Efficient Link Prediction in Real-World Network Data
DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs
Similarity Based Hierarchical Clustering with an Application to Text Collections
Determining Data Relevance Using Semantic Types and Graphical Interpretation Cues
A First Step Toward Quantifying the Climate's Information Production over the Last 68,000 Years
HAUCA Curves for the Evaluation of Biomarker Pilot Studies with Small Sample Sizes and Large Numbers of Features
Stability Evaluation of Event Detection Techniques for Twitter
IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures
An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data
Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance
Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering. .
Contributor:
Boström, Henrik, editor., Editor,
Knobbe, Arno. editor., Editor,
Soares, Carlos, editor., Editor,
Papapetrou, Panagiotis. editor., Editor,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
ISBN:
978-3-319-46349-0
9783319463490
Publisher Number:
10.1007/978-3-319-46349-0 doi
Access Restriction:
Restricted for use by site license.