Overview

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

Full Product Details

Author:   Leong Hou U. ,  Hady W. Lauw
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Volume:   11607
Weight:   0.581kg
ISBN:  

9783030261412

ISBN 10:   3030261417
Pages:   366
Publication Date:   12 September 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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. 

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