✦ LIBER ✦

Kernel self-optimization learning for kernel-based feature extraction and recognition

✍ Scribed by Li, Jun-Bao; Wang, Yun-Heng; Chu, Shu-Chuan; Roddick, John F.

Book ID: 121503452
Publisher: Elsevier Science
Year: 2014
Tongue: English
Weight: 795 KB
Volume: 257
Category: Article
ISSN: 0020-0255
DOI: 10.1016/j.ins.2013.08.011

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Kernel Self-optimized Locality Preservin

Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition

✍ Jun-Bao Li; Jeng-Shyang Pan; Shyi-Ming Chen 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 720 KB

Feature extraction of kernel regress rec

Feature extraction of kernel regress reconstruction for fault diagnosis based on self-organizing manifold learning

✍ Chen, Xiaoguang; Liang, Lin; Xu, Guanghua; Liu, Dan 📂 Article 📅 2013 🏛 Chinese Mechanical Engineering Society 🌐 English ⚖ 704 KB

Kernel-based learning for biomedical rel

Kernel-based learning for biomedical relation extraction

✍ Jiexun Li; Zhu Zhang; Xin Li; Hsinchun Chen 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 883 KB

## Abstract Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedi‐cal lex

Kernel optimization-based discriminant a

Kernel optimization-based discriminant analysis for face recognition

✍ Jun-Bao Li; Jeng-Shyang Pan; Zhe-Ming Lu 📂 Article 📅 2009 🏛 Springer-Verlag 🌐 English ⚖ 439 KB

Complete discriminant evaluation and fea

Complete discriminant evaluation and feature extraction in kernel space for face recognition

✍ Xudong Jiang; Bappaditya Mandal; Alex Kot 📂 Article 📅 2007 🏛 Springer-Verlag 🌐 English ⚖ 661 KB

Nonlinear feature extraction based on ce

Nonlinear feature extraction based on centroids and kernel functions

✍ Cheong Hee Park; Haesun Park 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 266 KB

A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of classes, providing dramatic savings in computational costs. The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kerne