Autor/Autorin: Tang Ke
Ke Tang, Ph.D: Obtained his Ph.D degree from Nanyang nTechnological University, Singapore. He is currently an nassociate professor with the School of Computer Science and nTechnology, University of Science and Technology of China n(USTC), Hefei, China. His research interests include nmachine learning, evolutionary computation and data mining.
Kernel methods are a new family of techniques with sound theoretical grounds. They have been shown to be powerful approaches to pattern classification problems. However, many of the newly created kernel methods are far from perfect, andextensions and improvements are always required to make them even more effective. This book investigates one important class of the kernel methods, the least square support vector machines (LS-SVM), and enhances its performance extensively. In particular, the LS-SVM is enhanced in the contexts of four sub-problems related to solving the pattern classification problem. That is, model selection, feature selection, building sparse kernel classifier and kernel classifier ensemble. The LS-SVM can be regarded as a representative of many other kernel methods, and thus many ideas presented in this book can be easily extended to enhance performance of those related kernel methods. The results obtained should be useful to professionals that work on the theoretical aspects of kernel methods, or anyone else who may be considering ustilizing kernel methods for real-world pattern classification problems.
Kernel methods are a new family of techniques
with sound theoretical grounds. They
have been shown to be powerful approaches to pattern
classification problems. However, many of the newly
created kernel methods are far from perfect, and
extensions and improvements are always required to
make them even more effective. This book
investigates one important class of the kernel
methods, the least square support vector
machines (LS-SVM), and enhances its
performance extensively. In particular, the LS-SVM
is enhanced in the contexts of four sub-problems
related to solving the pattern classification
problem. That is, model selection, feature
selection, building sparse kernel classifier and
kernel classifier ensemble. The LS-SVM can be
regarded as a representative of many other kernel
methods, and thus many ideas presented in this book
can be easily extended to enhance performance of
those related kernel methods. The results obtained
should be useful to professionals that work on the
theoretical aspects of kernel methods, or anyone
else who may be considering ustilizing kernel
methods for real-world pattern classification
problems.
Ke Tang, Ph.D: Obtained his Ph.D degree from Nanyang
Technological University, Singapore. He is currently an
associate professor with the School of Computer Science and
Technology, University of Science and Technology of China
(USTC), Hefei, China. His research interests include
machine learning, evolutionary computation and data mining.
Über den Autor
Ke Tang, Ph.D: Obtained his Ph.D degree from Nanyang Technological University, Singapore. He is currently an associate professor with the School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, China. His research interests include machine learning, evolutionary computation and data mining.
Klappentext
Kernel methods are a new family of techniques with sound theoretical grounds. They have been shown to be powerful approaches to pattern classification problems. However, many of the newly created kernel methods are far from perfect, andextensions and improvements are always required to make them even more effective. This book investigates one important class of the kernel methods, the least square support vector machines (LS-SVM), and enhances its performance extensively. In particular, the LS-SVM is enhanced in the contexts of four sub-problems related to solving the pattern classification problem. That is, model selection, feature selection, building sparse kernel classifier and kernel classifier ensemble. The LS-SVM can be regarded as a representative of many other kernel methods, and thus many ideas presented in this book can be easily extended to enhance performance of those related kernel methods. The results obtained should be useful to professionals that work on the theoretical aspects of kernel methods, or anyone else who may be considering ustilizing kernel methods for real-world pattern classification problems.
