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统计学习方法(第二版) 李航

每一单元的知识要点整理课后习题解答,以及算法的Python实现

第一篇 监督学习

  • Chapter1 统计学习及监督学概论
  • Chapter2 感知机
  • Chapter3 k近邻法
  • Chapter4 朴素贝叶斯法
  • Chapter5 决策树
  • Chapter6 逻辑斯谛回归与最大熵模型
  • Chapter7 支持向量机
  • Chapter8 提升方法
  • Chapter9 EM算法及其推广
  • Chapter10 隐马尔可夫模型
  • Chapter11 条件随机场
  • Chapter12 监督学习方法总结

第二篇 无监督学习

  • Chapter13 无监督学习概论
  • Chapter14 聚类方法
  • Chapter15 奇异值分解
  • Chapter16 主成分分析
  • Chapter17 潜在语义分析
  • Chapter18 概率潜在语义分析
  • Chapter19 马尔可夫链蒙特卡罗法
  • Chapter20 潜在狄利克雷分配
  • Chapter21 PageRank算法
  • Chapter22 无监督学习方法总结

参考资料

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