Book Volume 9651 2016

Advances in Knowledge Discovery and Data Mining

20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I

ISBN: 978-3-319-31752-6 (Print) 978-3-319-31753-3 (Online)

Table of contents (47 chapters)

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  1. Front Matter

    Pages I-XXIV

  2. Classification

    1. Front Matter

      Pages 1-1

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      Pages 3-13

      Joint Classification with Heterogeneous Labels Using Random Walk with Dynamic Label Propagation

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      Pages 14-26

      Hybrid Sampling with Bagging for Class Imbalance Learning

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      Pages 27-39

      Sparse Adaptive Multi-hyperplane Machine

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      Pages 40-51

      Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior

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      Pages 52-64

      Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning

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      Pages 65-76

      Locally Weighted Ensemble Learning for Regression

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      Pages 77-88

      Reliable Confidence Predictions Using Conformal Prediction

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      Pages 89-101

      Grade Prediction with Course and Student Specific Models

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      Pages 102-114

      Flexible Transfer Learning Framework for Bayesian Optimisation

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      Pages 115-126

      A Simple Unlearning Framework for Online Learning Under Concept Drifts

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      Pages 127-138

      User-Guided Large Attributed Graph Clustering with Multiple Sparse Annotations

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      Pages 139-151

      Early-Stage Event Prediction for Longitudinal Data

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      Pages 152-164

      Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework

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      Pages 165-176

      Shot Boundary Detection Using Multi-instance Incremental and Decremental One-Class Support Vector Machine

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      Pages 177-188

      Will I Win Your Favor? Predicting the Success of Altruistic Requests

  3. Feature Extraction and Pattern Mining

    1. Front Matter

      Pages 189-189

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      Chapter

      Pages 191-202

      Unsupervised and Semi-supervised Dimensionality Reduction with Self-Organizing Incremental Neural Network and Graph Similarity Constraints

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      Pages 203-214

      Cross-View Feature Hashing for Image Retrieval

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      Pages 215-226

      Towards Automatic Generation of Metafeatures

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      Pages 227-238

      Hash Learning with Convolutional Neural Networks for Semantic Based Image Retrieval

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      Chapter

      Pages 239-252

      Bayesian Group Feature Selection for Support Vector Learning Machines

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