# Online SVR
Methods adapted from the paper "Accurate On-line Support Vector Regression" from Junshui Ma, James Theiler, and Simon Perkins. This code is essentially a Python rewrite of Francesco Parrella's MATLAB code http://onlinesvr.altervista.org/. Includes an undocumented application to cluster-size prediction in traffic flow. Used for a group project in 16-831 Statistical Techniques in Robotics at Carnegie Mellon University.
实现准确的在线支持向量回归 Implementation of Accurate Online Support Vector Regression in Python. python.zip （31个子文件） 
code_resource_0100 dataset 
phase0_10D_prediction_log.txt 10KB phase0_10D.txt 165KB phase1_5D_PA.txt 9KB phase0_10D_PA.txt 9KB phase1_5D.txt 88KB phase1_10D.txt 164KB phase1_10D_PA.txt 9KB phase0_5D.txt 88KB phase0_5D_PA.txt 9KB phase1_10D_prediction_log.txt 11KB data_preparation new_log.txt 73KB
traffic_data.mat 128KB output2.txt 270KB pa_loss.m 129B test_traffic_flow.m 3KB test_traffic_flow_PA.m 1KB avg_arrival_rate.m 192B output1.txt 268KB .gitignore 702B animated_plot.py 1KB traffic_prediction.py 4KB traffic_code dectlc_clusters.txt 17KB test_rcv.py 65B listener.py 2KB JobBlockMData.java 16KB parse_surtrac.py 2KB centre_graham_cluster_log.txt 565KB parse_surtrac_PA.py 3KB service.py 9KB README.md 433B online_svr.py 23KB资源评论
