TY - GEN
T1 - Choosing of battle position through training support vector machines
AU - Li, Kan
AU - Liu, Yu Shu
PY - 2002
Y1 - 2002
N2 - Battlefield environment affects maneuver and war situation. We choose the battle position though training support vector machine (SVM). We give firstly the method of two-class classification by means of SVM. Choosing of battle position is a problem of multi-class classification, so we adopt "divide and conquer" approach to train the data set. We use recursively SVM until the result meets the condition of battle position. In the course of training the data, we use one-against-one and the decision tree methods. Using the schemes, we select the suited battle position and get better effect.
AB - Battlefield environment affects maneuver and war situation. We choose the battle position though training support vector machine (SVM). We give firstly the method of two-class classification by means of SVM. Choosing of battle position is a problem of multi-class classification, so we adopt "divide and conquer" approach to train the data set. We use recursively SVM until the result meets the condition of battle position. In the course of training the data, we use one-against-one and the decision tree methods. Using the schemes, we select the suited battle position and get better effect.
KW - Optimal separating hyperplane
KW - Support vector machine (SVM)
KW - Training algorithm
UR - http://www.scopus.com/inward/record.url?scp=0036921103&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0036921103
SN - 0780375084
T3 - Proceedings of 2002 International Conference on Machine Learning and Cybernetics
SP - 18
EP - 20
BT - Proceedings of 2002 International Conference on Machine Learning and Cybernetics
T2 - Proceedings of 2002 International Conference on Machine Learning and Cybernetics
Y2 - 4 November 2002 through 5 November 2002
ER -