<p><p>Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separa
Optimization Based Data Mining: Theory and Applications
β Scribed by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li (auth.)
- Publisher
- Springer-Verlag London
- Year
- 2011
- Tongue
- English
- Leaves
- 333
- Series
- Advanced Information and Knowledge Processing
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Subjects
Data Mining and Knowledge Discovery; Input/Output and Data Communications
π SIMILAR VOLUMES
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations.
<p><P>The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human acti
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem stud
<p>Β·Β Β Β Β Β Β Β Β This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial info