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Advances in Intelligent Data Analysis XV de Henrik Boström

Advances in Intelligent Data Analysis XV: Lecture Notes in Computer Science, cartea 9897

Editat de Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou
en Limba Engleză Paperback – 21 sep 2016
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. 
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Specificații

ISBN-13: 9783319463483
ISBN-10: 3319463489
Pagini: 420
Ilustrații: XIII, 404 p. 146 illus.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.63 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.