统计学习方法及代码实现(Python)

[{"title":"（ 19 个子文件 17.26MB ） 统计学习方法及代码实现(Python)","children":[{"title":"统计学习方法及代码实现(Python)","children":[{"title":"统计学习方法.pdf <span style='color:#111;'> 17.56MB </span>","children":null,"spread":false},{"title":"code","children":[{"title":"readme.md <span style='color:#111;'> 555B </span>","children":null,"spread":false},{"title":"gongzhong.jpg <span style='color:#111;'> 8.06KB </span>","children":null,"spread":false},{"title":"第9章 EM算法及其推广(EM)","children":[{"title":"em.ipynb <span style='color:#111;'> 6.92KB </span>","children":null,"spread":false}],"spread":true},{"title":"第11章 条件随机场(CRF)","children":[{"title":"CRF.ipynb <span style='color:#111;'> 3.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"第1章 统计学习方法概论(LeastSquaresMethod)","children":[{"title":"least_sqaure_method.ipynb <span style='color:#111;'> 116.01KB </span>","children":null,"spread":false}],"spread":true},{"title":"第5章 决策树(DecisonTree)","children":[{"title":"DT.ipynb <span style='color:#111;'> 35.75KB </span>","children":null,"spread":false},{"title":"Decision Tree (ID3 剪枝) <span style='color:#111;'> 7.28KB </span>","children":null,"spread":false},{"title":"mytree.pdf <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false}],"spread":true},{"title":"第2章 感知机(Perceptron)","children":[{"title":"Iris_perceptron.ipynb <span style='color:#111;'> 52.69KB </span>","children":null,"spread":false}],"spread":true},{"title":"第7章 支持向量机(SVM)","children":[{"title":"support-vector-machine.ipynb <span style='color:#111;'> 27.49KB </span>","children":null,"spread":false}],"spread":true},{"title":"第3章 k近邻法(KNearestNeighbors)","children":[{"title":"KNN.ipynb <span style='color:#111;'> 60.21KB </span>","children":null,"spread":false},{"title":"KDT.py <span style='color:#111;'> 3.67KB </span>","children":null,"spread":false}],"spread":true},{"title":"第10章 隐马尔可夫模型(HMM)","children":[{"title":"HMM.ipynb <span style='color:#111;'> 12.61KB </span>","children":null,"spread":false}],"spread":true},{"title":"第4章 朴素贝叶斯(NaiveBayes)","children":[{"title":"GaussianNB.ipynb <span style='color:#111;'> 8.45KB </span>","children":null,"spread":false}],"spread":true},{"title":"第6章 逻辑斯谛回归(LogisticRegression)","children":[{"title":"LR.ipynb <span style='color:#111;'> 39.76KB </span>","children":null,"spread":false},{"title":"最大熵模型 IIS.py <span style='color:#111;'> 4.25KB </span>","children":null,"spread":false}],"spread":false},{"title":"第8章 提升方法(AdaBoost)","children":[{"title":"Adaboost.ipynb <span style='color:#111;'> 24.45KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":"readme.txt <span style='color:#111;'> 17B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]