Trends and applications in knowledge discovery and data mining : PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM Jeju, South Korea, May 23, 2017, Revised selected papers /: PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM Jeju, South Korea, May 23, 2017, Revised selected papers. (2017)

• Record Type: Book
• Further Information: Note: Edited by U Kang, Ee-Peng Lim, Jeffrey Xu Yu, Yang-Sae Moon.
• Editors: Kang, U; Lim, Ee-Peng; Yu, Jeffrey Xu; Moon, Yang-Sae
• Other Names: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 21st; Workshop on Machine Learning for Sensory Data Analysis; Workshop on Biologically Inspired Data-Mining Techniques; Workshop on Data Mining in Business Process Management
• Contents: Early Classification of Multivariate Time Series on Distributed and In-Memory Platforms -- Behavior Classification of Dairy Cows fitted with GPS collars -- Dynamic Real-time Segmentation and Recongnition of Activities using a Multi-feature Windowing Approach -- Feature Extraction from EEG data for a P300 Based Brain-computer Interface -- Thermal Stratification Prediction at Lake Trevallyn -- Development of a Software Vulnerability Prediction Web Service based on Artificial Neural Networks -- Diversification Heuristics in Bees Swarm Optimization for Association Rules Mining -- Improved CFDP Algorithms Based on Shared Nearest Neighbors and Transitive Closure -- CNN-based Sequence Labeling for Fine-grained Opinion Mining of Microblogs -- A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles -- Self-Adaptive Weighted Extreme Learning Machine for Imbalanced Classification Problems -- Estimating Word Probabilities with Neural Networks in Latent Dirichlet Allocation -- GA-Apriori: Combining Apriori Heuristic and Genetic Algorithms for Solving the Frequent Itemsets Mining Problem -- Shelf Time Analysis in CTP Insurance Claims Processing -- Automated Product-Attribute Mapping -- A Novel Extreme Learning Machine-based Classification Algorithm for Uncertain Data -- SPGLAD: A Self-Paced Learning-based Crowdsourcing Classification Model.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2017
• Extent: 1 online resource (xiv, 203 pages), illustrations
• Subjects: 006.3/12; Computer science; Data mining -- Congresses; Data mining; Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Health Informatics; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Computation by Abstract Devices; Computers -- Database Management -- Data Mining; Medical -- General; Computers -- System Administration -- Storage & Retrieval; Computers -- Information Technology; Computers -- Machine Theory; Data mining; Health & safety aspects of IT; Information retrieval; User interface design & usability; Artificial intelligence; Data mining; Medical records_xData processing; Information storage and retrieva; Computers -- Intelligence (AI) & Semantics; Artificial intelligence; Electronic books; Conference papers and proceedings
• Languages: English
• ISBNs: 9783319672748; 3319672746; 3319672738; 9783319672731
• Related ISBNs: 9783319672731
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed October 12, 2017).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.