Trends and Applications in Knowledge Discovery and Data Mining

PAKDD 2019 Workshops, BDM, DLKT, LDRC, PAISI, WeL, Macau, China, April 14–17, 2019, Revised Selected Papers

AvLeong Hou U.,Hady W. Lauw

Häftad, Engelska, 2019

Del 11607 i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, in Macau, China, in April 2019.The 31 revised papers presented were carefully reviewed and selected from a total of 52 submissions. They stem from the following workshops: · PAISI 2019: 14th Pacific Asia Workshop on Intelligence and Security Informatics· WeL 2019: PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future· LDRC 2019: PAKDD 2019 Workshop on Learning Data Representation for Clustering· BDM 2019: 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining· DLKT 2019: 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer

Produktinformation

Utgivningsdatum:2019-09-12
Mått:155 x 235 x 21 mm
Vikt:575 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:366
Förlag:Springer Nature Switzerland AG
ISBN:9783030261412

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019).- A Supporting Tool for IT System Security Specification Evaluation Based on ISO/IEC 15408 and ISO/IEC 18045.- An Investigation on Multi View based User Behavior towards Spam Detection in Social Networks.- A Cluster Ensemble Strategy for Asian Handicap Betting.- Designing an Integrated Intelligence Center: New Taipei City Police Department as an Example.- Early Churn User Classification in Social Networking Service Using Attention-based Long Short-Term Memory.- PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019).- Weakly Supervised Learning by a Confusion Matrix of Contexts.- Learning a Semantic Space for Modeling Images,Tags and Feelings in Cross-media Search.- Adversarial Active Learning in the Presence of Weak and Malicious Oracles.- The Most Related Knowledge First: A Progressive Domain Adaptation Method.- Learning Data Representation for Clustering (LDRC 2019).- Deep Architectures for Joint Clustering and Visualization with Self-Organizing Maps.- Deep cascade of extra trees.- Algorithms for an Efficient Tensor Biclustering.- Change point detetion in periodic panel data using a mixture-model-based approach.- The 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019).- Neural Network-Based Deep Encoding for Mixed-Attribute Data Classification.- Protein Complexes Detection Based on Deep Neural Network.- Predicting Auction Price of Vehicle License Plate with Deep Residual Learning.- Mining Multispectral Aerial Images for Automatic Detection of Strategic Bridge Locations for Disaster Relief Missions.- Chinese Word Segmentation with Feature Alignment.- Spike Sorting with Locally Weighted Co-association Matrix-based Spectral Clustering.- Label Distribution Learning Based Age-Invariant Face Recognition.- Overall Loss For Deep Neural Networks.- Sentiment Analysis Based on LSTM Architecture with Emoticon Attention.- Aspect Level Sentiment Analysis with Aspect Attention.- The 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer (DLKT 2019).- Transfer Channel Pruning for Compressing Deep Domain Adaptation Models.- A Heterogeneous Domain Adversarial Neural Network for Trans-Domain Behavioral Targeting.- Natural Language Business Intelligence Question Answering through SeqtoSeq Transfer Learning.- Robust Faster R-CNN:Increasing Robustness to Occlusions and multi-scale objects.- Effectively Representing Short Text via the Improved Semantic Feature Space Mapping.- Probabilistic Graphical Model Based Highly Scalable Directed Community Detection Algorithm.- Hilltop based recommendation in co-author networks.- Neural Variational Collaborative Filtering for Top-K Recommendation.