Table of Contents
Table of Contents
Preface
Chapter 1. Constrained Data Self-Representative Graph Construction (pp. 1-20)
(L. Weng, F. Dornaika and Z. Jin)
Chapter 2. Injecting Randomness into Graphs: An Ensemble Semi-Supervised Learning Framework (pp. 21-44)
(Qin Zhang, Jianyuan Sun, Guoqiang Zhong, Junyu Dong, Feng Gao, Huizhen Yang and Hina Saeeda)
Chapter 3. Label Propagation via Kernel Flexible Manifold Embedding (pp. 45-66)
(F. Dornaika, Y. El Traboulsi, I. Arganda-Carreras)
Chapter 4. Fast Graph-Based Semi-Supervised Learning and Its Applications (pp. 67-98)
(Yan-Ming Zhang, Kaizhu Huang, Kaizhu Huang, Guang-Gang Geng and Cheng-Lin Liu)
Chapter 5. Semi-Supervised Learning in Two-Class Classification with a Scarce Population Class (pp. 99-122)
(Addisson Salazar, Gonzalo Safont and Luis Vergara)
Chapter 6. Self-Training Field Pattern Prediction based on Kernel Methods (pp. 123-170)
(Haochuan Jiang, Kaizhu Huang, Xu-yao Zhang and Rui Zhang)
Chapter 7. Semi-Supervised Learning via Multi-Modal Curriculum Generation (pp. 171-206)
(Chen Gong)
Chapter 8. Semi-Supervised Learning on Big Multimedia Data for Situation Recognition (pp. 207-222)
(Mengfan Tang)
About the Editors (pp. 223-224)
Index (pp. 225)
Additional Information
Keywords: Semi-supervised learning; Graph construction; Transductive learning; curriculum learning; manifold learning
This book is suitable for University students (undergraduate or graduate) in computer science, statistics, electrical engineering, or any one else who learns or is potential to use machine learning algorithms; It could be of special interest to professors, who research on artificial intelligence, pattern recognition, machine learning, data mining, and related fields, or engineers, who apply machine learning models into their products.