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Semi-Supervised Learning - Background, Applications and Future Directions (Hardcover)
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Semi-Supervised Learning - Background, Applications and Future Directions (Hardcover)
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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.
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