李航 统计学习方法

[{"title":"（ 27 个子文件 32.47MB ） 李航 统计学习方法","children":[{"title":"李航","children":[{"title":"统计学习方法","children":[{"title":"统计学习方法-代码","children":[{"title":"第10章 隐马尔可夫模型","children":[{"title":"HMM.ipynb <span style='color:#111;'> 12.61KB </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8章 提升方法(AdaBoost)","children":[{"title":"Adaboost.ipynb <span style='color:#111;'> 24.45KB </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;'> 116.00KB </span>","children":null,"spread":false}],"spread":true},{"title":"第3章 k近邻法(KNearestNeighbors)","children":[{"title":"KNN.ipynb <span style='color:#111;'> 32.61KB </span>","children":null,"spread":false}],"spread":true},{"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11章 条件随机场","children":[{"title":".ipynb_checkpoints","children":[{"title":"CRF-checkpoint.ipynb <span style='color:#111;'> 3.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"CRF.ipynb <span style='color:#111;'> 3.15KB </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;'> 27.43KB </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}],"spread":true},{"title":"代码目录.txt <span style='color:#111;'> 459B </span>","children":null,"spread":false},{"title":"第5章 决策树(DecisonTree)","children":[{"title":"mytree.pdf <span style='color:#111;'> 1.07KB </span>","children":null,"spread":false},{"title":"DT.ipynb <span style='color:#111;'> 35.75KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":"统计学习方法-课件","children":[{"title":"第1章 统计学习方法概论.pdf <span style='color:#111;'> 1.19MB </span>","children":null,"spread":false},{"title":"第11章 条件随机场.pdf <span style='color:#111;'> 2.64MB </span>","children":null,"spread":false},{"title":"第9章 EM算法及其推广.pdf <span style='color:#111;'> 1.91MB </span>","children":null,"spread":false},{"title":"第2章 感知机.pdf <span style='color:#111;'> 1.63MB </span>","children":null,"spread":false},{"title":"第12章 统计学习方法总结.pdf <span style='color:#111;'> 928.37KB </span>","children":null,"spread":false},{"title":"第8章 提升方法.pdf <span style='color:#111;'> 2.12MB </span>","children":null,"spread":false},{"title":"第3章 k 近邻法.pdf <span style='color:#111;'> 1.49MB </span>","children":null,"spread":false},{"title":"第7章 支持向量机.pdf <span style='color:#111;'> 3.30MB </span>","children":null,"spread":false},{"title":"第4章 朴素贝叶斯法.pdf <span style='color:#111;'> 1.05MB </span>","children":null,"spread":false},{"title":"第6章 Logistic回归.pdf <span style='color:#111;'> 2.08MB </span>","children":null,"spread":false},{"title":"第10章 隐马尔科夫模型.pdf <span style='color:#111;'> 1.96MB </span>","children":null,"spread":false},{"title":"第5章 决策树-2016-ID3CART.pdf <span style='color:#111;'> 2.04MB </span>","children":null,"spread":false}],"spread":false},{"title":"jianyi","children":null,"spread":false},{"title":"统计学习方法(李航)完整版带目录书签.pdf <span style='color:#111;'> 17.56MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]