Haijin Fan

13 papers B 2C 2Misc 1Journal 8
YearRankTypeTitle / Venue / Authors
2017 J jnl
Robust Recurrent Kernel Online Learning.
IEEE Trans. Neural Networks Learn. Syst.
Qing Song, Zhao Xu, Haijin Fan, Danwei Wang
2016 J jnl
Cardiac image segmentation by random walks with dynamic shape constraint.
IET Comput. Vis.
XuLei Yang, Yi Su, Rubing Duan, Haijin Fan, Si Yong Yeo, Calvin Chi-Wan Lim, Liang Zhong, Ru-San Tan
2016 J jnl
Kernel online learning with adaptive kernel width.
Neurocomputing
Haijin Fan, Qing Song, Sumit Bam Shrestha
2015 J jnl
Kernel online learning algorithm with state feedbacks.
Knowl. Based Syst.
Haijin Fan, Qing Song, XuLei Yang, Zhao Xu
2014 J jnl
A linear recurrent kernel online learning algorithm with sparse updates.
Neural Networks
Haijin Fan, Qing Song
2014 J jnl
An information theoretic sparse kernel algorithm for online learning.
Expert Syst. Appl.
Haijin Fan, Qing Song, Zhao Xu
2014 J jnl
Online learning with kernel regularized least mean square algorithms.
Knowl. Based Syst.
Haijin Fan, Qing Song, Sumit Bam Shrestha
2013 J jnl
A sparse kernel algorithm for online time series data prediction.
Expert Syst. Appl.
Haijin Fan, Qing Song
2012 C conf
A training algorithm and stability analysis for recurrent neural networks.
FUSION
Zhao Xu, Qing Song, Danwei Wang, Haijin Fan
2012 B conf
An information theoretic kernel algorithm for robust online learning.
IJCNN
Haijin Fan, Qing Song, Zhao Xu
2012 B conf
Online prediction of time series data with recurrent kernels.
IJCNN
Zhao Xu, Qing Song, Haijin Fan, Danwei Wang
2012 C conf
Recurrent online kernel recursive least square algorithm for nonlinear modeling.
IECON
Haijin Fan, Qing Song, Zhao Xu
2009 Misc conf
A Novel System of Stereoscopic Video Based on TMS320DM642 DSP.
ICIG
Haijin Fan, Fan Yang, Qingmin Liao
redb/extractors/js_extractors/scripts/package.json
← Index redb/extractors/js_extractors/scripts/package.json json 
{
  "name": "redb-js-xray-runner",
  "private": true,
  "description": "Node bridge that runs @nodesecure/js-x-ray for the Python JS pipeline. Run `npm install` here once, then point JS_XRAY_RUNNER_PATH at js-xray-runner.js (it defaults to this directory).",
  "version": "0.1.0",
  "main": "js-xray-runner.js",
  "scripts": {
    "postinstall": "patch-package"
  },
  "dependencies": {
    "@nodesecure/js-x-ray": "^7.0.0",
    "patch-package": "^8.0.1"
  }
}