Lazy Man's SVM | add bias term and regulation term, based on http://www.weibo.com/1459604443/A3x1VtIQn

lazy_man_svm.R

set.seed(1001)
x = rbind(cbind(rnorm(50, 1, 0.3), rnorm(50, 2, 0.3)), cbind(rnorm(50, 2, 0.3), rnorm(50, 1, 0.3)))
y = c(rep(1, 50), rep(-1, 50))

h <- function(param, C = 1){
  b = param[length(param)]
  w = param[-length(param)]
  g = y * (x %*% w + b)
  0.5 * w %*% w + C * sum(1 - g[g < 1])
}

par(mfrow = c(2,2))
regulation = c(1, 5, 10, 100)
for(i in 1:4){
  res = optim(c(0, 0, 0), h, C = regulation[i], method = "L-BFGS-B")
  param = res$par
  w = param[1:2]
  b = param[3]
  line.x = seq(min(x[,1]), max(x[,2]), 0.1)
  line.y1 = (1 - line.x * w[1] - b) / w[2]
  line.y2 = (-1 - line.x * w[1] - b) / w[2]
  plot(x, col = y + 2, main = paste("C = ", regulation[i], sep = ""))
  points(line.x, line.y1, type = "l")
  points(line.x, line.y2, type = "l")
}

g = y * (x %*% w + b) 在加入soft margin理念之后，为什么g可以存在小于1？ 支持向量的g应该等于1， 非支持向量的应该大于1. 小于1的，岂不是噪音数据？

line.x = seq(min(x), max(x), 0.1) 不知道这样做line.x = seq(min(x[,1]), max(x[,1]), 0.1)是否更为妥当？我已经fork了你的代码，我附带了一些注释，我的理解大概是如此的，盼指点～

你的修改是对的，第一个comment已邮件回复。