The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 21st International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes, theories and applications, cognitive robotics, and learning systems for social network and web mining.

The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes,theories and applications, cognitive robotics, and learning systems for social network and web mining.

This book constitutes the refereed proceedings of the 10th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2019, held in Guangzhou, China, in July 2019. The 57 papers presented in this volume were carefully reviewed and selected from 129 submissions. The papers are organized in topical sections named: neural computation; biologically inspired systems; image recognition: detection, tracking and classification; and data analysis and natural language processing.

The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes, theories and applications, cognitive robotics, and learning systems for social network and web mining.

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very scarce labeled data. The book focuses on some state-of-the-art research on semi-supervised learning. In the first chapter, Weng, Dornaika and Jin introduce a graph construction algorithm named the constrained data self-representative graph construction (CSRGC). In the second chapter, to reduce the graph construction complexity, Zhang et al. use anchors that were a special subset chosen from the original data to construct the full graph, while randomness was injected into graphs to improve the classification accuracy and deal with the high dimensionality issue. In the third chapter, Dornaika et al. introduces a kernel version of the Flexible Manifold Embedding (KFME) algorithm. In the fourth chapter, Zhang et al. present an efficient and robust graph-based transductive classification method known as the minimum tree cut (MTC), for large scale applications. In the fifth chapter, Salazar, Safont and Vergara investigated the performance of semi-supervised learning methods in two-class classification problems with a scarce population of one of the classes. In the sixth chapter, by breaking the sample identically and independently distributed (i.i.d.) assumption, one novel framework called the field support vector machine (F-SVM) with both classification (F-SVC) and regression (F-SVR) purposes is introduced. In the seventh chapter, Gong employs the curriculum learning methodology by investigating the difficulty of classifying every unlabeled example. As a result, an optimized classification sequence was generated during the iterative propagations, and the unlabeled examples are logically classified from simple to difficult. In the eighth chapter, Tang combines semi-supervised learning with geo-tagged photo streams and concept detection to explore situation recognition. This book is suitable for university students (undergraduate or graduate) in computer science, statistics, electrical engineering, or anyone else who would potentially use machine learning algorithms; professors, who research artificial intelligence, pattern recognition, machine learning, data mining and related fields; and engineers, who apply machine learning models into their products.