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Home > Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I > Sparse Adaptive Multi-hyperplane Machine
Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I |
James Bailey; Latifur Khan; Takashi Washio; Gill Dobbie; Joshua Zhexue Huang; Ruili Wang
Springer International Publishing
Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I
Sparse Adaptive Multi-hyperplane Machine
Authors

Khanh Nguyen; Trung Le; Vu Nguyen; Dinh Phung

ARI Id

1664726248327_2951240

Access

Not Available Free

Pages

27-39

DOI

10.1007/978-3-319-31753-3_3

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-319-31753-3_3

Table of Contents of Book
Showing 1 to 20 of 53 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
I-XXIV
Front Matter
1-1
Joint Classification with Heterogeneous Labels Using Random Walk with Dynamic Label Propagation
Yongxin Liao; Shenxi Yuan; Jian Chen; Qingyao Wu; Bin Li
3-13
Hybrid Sampling with Bagging for Class Imbalance Learning
Yang Lu; Yiu-ming Cheung; Yuan Yan Tang
14-26
Sparse Adaptive Multi-hyperplane Machine
Khanh Nguyen; Trung Le; Vu Nguyen; Dinh Phung
27-39
Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior
Qinzhe Zhang; Qin Zhang; Guodong Long; Peng Zhang; Chengqi Zhang
40-51
Optimal Training and Efficient Model Selection for Parameterized Large Margin Learning
Yuxun Zhou; Jae Yeon Baek; Dan Li; Costas J. Spanos
52-64
Locally Weighted Ensemble Learning for Regression
Man Yu; Zongxia Xie; Hong Shi; Qinghua Hu
65-76
Reliable Confidence Predictions Using Conformal Prediction
Henrik Linusson; Ulf Johansson; Henrik Boström; Tuve Löfström
77-88
Grade Prediction with Course and Student Specific Models
Agoritsa Polyzou; George Karypis
89-101
Flexible Transfer Learning Framework for Bayesian Optimisation
Tinu Theckel Joy; Santu Rana; Sunil Kumar Gupta; Svetha Venkatesh
102-114
A Simple Unlearning Framework for Online Learning Under Concept Drifts
Sheng-Chi You; Hsuan-Tien Lin
115-126
User-Guided Large Attributed Graph Clustering with Multiple Sparse Annotations
Jianping Cao; Senzhang Wang; Fengcai Qiao; Hui Wang; Feiyue Wang; Philip S. Yu
127-138
Early-Stage Event Prediction for Longitudinal Data
Mahtab J. Fard; Sanjay Chawla; Chandan K. Reddy
139-151
Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework
Cheng Li; Sunil Gupta; Santu Rana; Wei Luo; Svetha Venkatesh; David Ashely et al.
152-164
Shot Boundary Detection Using Multi-instance Incremental and Decremental One-Class Support Vector Machine
Hanhe Lin; Jeremiah D. Deng; Brendon J. Woodford
165-176
Will I Win Your Favor? Predicting the Success of Altruistic Requests
Hsun-Ping Hsieh; Rui Yan; Cheng-Te Li
177-188
Front Matter
189-189
Unsupervised and Semi-supervised Dimensionality Reduction with Self-Organizing Incremental Neural Network and Graph Similarity Constraints
Zhiyang Xiang; Zhu Xiao; Yourong Huang; Dong Wang; Bin Fu; Wenjie Chen
191-202
Cross-View Feature Hashing for Image Retrieval
Wei Wu; Bin Li; Ling Chen; Chengqi Zhang
203-214
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 53 entries
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