Ling Jing

发表

Fitting B-spline curves by least squares support vector machines

Ling Jing, Li Sun, 2005, 2005 International Conference on Neural Networks and Brain.

Least Squares Twin Support Tensor Machine for Classification

Ling Jing, Xinbin Zhao, Haifa Shi, 2014 .

Least squares support tensor machine

Ling Jing, Haifa Shi, Xinbin Zhao, 2013 .

Image Classification Based on New Matrix Distance

Ling Jing, N. Deng, Xinbin Zhao, 2015 .

Ling Jing, J. Zhang, Bing Yang, 2014 .

Multiple Instance Learning via Multiple Kernel Learning *

Ling Jing, Bing Yang, Qian Li, 2010 .

Identifying translation initiation sites in prokaryotes using support vector machine.

Ling Jing, Yong Wang, Zhixia Yang, 2010, Journal of theoretical biology.

Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma.

C. Fu, Ling Jing, S. Deng, 2013, IET systems biology.

An ε-twin support vector machine for regression

Ling Jing, N. Deng, Y. Shao, 2012, Neural computing & applications (Print).

An incremental dimensionality reduction method on discriminant information for pattern classification

Ling Jing, Zhixia Yang, Xiaoqin Hu, 2009, Pattern Recognit. Lett..

Support Vector Machine Solving Freeform Curve and Surface Reconstruction Problem

Ling Jing, Zhixia Yang, Ling Jing, 2007, Int. J. Wavelets Multiresolution Inf. Process..

A novel projection nonparallel support vector machine for pattern classification

Liming Liu, Ling Jing, Ling Zhen, 2018, Eng. Appl. Artif. Intell..

Identification of oncogenic genes for colon adenocarcinoma from genomics data

Su Deng, Ling Jing, Guangxu Jin, 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).

Novel Grouping Method-based support vector machine plus for structured data

Ling Zhen, Qiuling Hou, Naiyang Deng, 2016, Neurocomputing.

Discriminative information-based nonparallel support vector machine

Ling Jing, Jinxin Zhang, Qiuling Hou, 2019, Signal Process..

Constructing support vector machine ensemble with segmentation for imbalanced datasets

Ling Jing, N. Deng, Bing Yang, 2013, Neural Computing and Applications.

Detection of Horizontal Gene Transfer in Bacterial Genomes

Ling Jing, Ling Jing, Bing Yang, 2009 .

Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

Ling Jing, Ling Zhen, Qiuling Hou, 2018 .

Semisupervised Dimension Reduction Based on Pairwise Constraint Propagation for Hyperspectral Images

Ling Jing, Qiuling Hou, Meng Lv, 2016, IEEE Geoscience and Remote Sensing Letters.

Predicting DNA- and RNA-binding proteins from sequences with kernel methods.

Ling Jing, Lingyun Wu, Xiaojian Shao, 2009, Journal of theoretical biology.

Robust weighted linear loss twin multi-class support vector regression for large-scale classification

Jinxin Zhang, Ling Zhen, Wenwen Qiang, 2020, Signal Process..

Global and local learning from positive and unlabeled examples

Ling Jing, Ting Ke, Lidong Zhang, 2018, Applied Intelligence.

Support vector machine with hypergraph-based pairwise constraints

Ling Jing, Ling Zhen, Qiuling Hou, 2016, SpringerPlus.

Twin Bounded Support Tensor Machine for Classification

Ling Jing, Ling Zhen, Haifa Shi, 2016, Int. J. Pattern Recognit. Artif. Intell..

Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Ling Jing, Xinbin Zhao, Meng Lv, 2017 .

Unsupervised double weight graphs based discriminant analysis for dimensionality reduction

Baocheng Li, Peng Zhang, Jinxin Zhang, 2019, International Journal of Remote Sensing.

An ε-twin support vector machine for regression

Yuan-Hai Shao, Ling Jing, Chunhua Zhang, 2012, Neural Computing and Applications.

Constructing support vector machine ensemble with segmentation for imbalanced datasets

Yi Li, Ling Jing, Qian Li, 2012, Neural Computing and Applications.

Network Kernel SVM for microarray classification and gene sets selection

Ling Jing, Bing Yang, Naiyang Deng, 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).

Building a Biased Least Squares Support Vector Machine Classifier for Positive and Unlabeled Learning

Ling Jing, Bing Yang, Ting Ke, 2014, J. Softw..