Learning And Optimization Of The Kernel Functions From Insufficiently Labeled Data
Amongst all the machine learning techniques, kernel methods are increasingly becoming popular due to their efficiency, accuracy and ability to handle high-dimensional data. The fundamental problem related to these learning techniques is the selection of the kernel function. Therefore, learning th...
Saved in:
主要作者: | Abbasnejad, M. Ehsan |
---|---|
格式: | Thesis |
語言: | English |
出版: |
2010
|
主題: | |
在線閱讀: | http://eprints.usm.my/41234/1/M._Ehsan_Abbasnejad-shahfiq.pdf |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
An optimized framework for header suppression of real time IPV6 traffic in multiprotocol label switching (MPLS) networks.
由: Mohammed, Imad Jasim
出版: (2011) -
Finding Best Semantic Relatedness Functions For Schema Matchers
由: Emadzadeh, Ehsan
出版: (2010) -
Spatial Kernel-based Generalized C-mean Clustering for Medical Image Segmentation.
由: Lee, Song Yeow
出版: (2010) -
Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
由: Mohammed, Alhassan Afnan
出版: (2022) -
A Data Grid Replica Management System With
Local And Global Multi-Objective Optimization
由: E. Almistarihi, Husni Hamad
出版: (2009)