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Exercises answers to the book "machine-learning" written by Prof. Zhou Zhihua of Nanjing University
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# Machine-Learning (Zhou Zhihua)（周志华《机器学习》课后练习） ----- My answers (i.e. ideas, code) for the book "Machine Learning (机器学习)" written by Prof. Zhou Zhihua. ps. all the code exercises are implemented by **Python** in **eclipse-pydev** env. for more info: welcome to my blog: [PnYuan - 周志华《机器学习》习题解答](https://pnyuan.github.io/blog/categories/%E5%91%A8%E5%BF%97%E5%8D%8E%E3%80%8A%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%8B%E4%B9%A0%E9%A2%98%E8%A7%A3%E7%AD%94/) and [CSDN - 周志华《机器学习》课后习题解答系列](http://blog.csdn.net/snoopy_yuan/article/details/62045353) (更新中...2017-7-25) ---- ### [Ch6.Support Vector Machine (支持向量机)](./ch6_support_vector_machine/) ### 练习包括(exercises include)： - 支持向量分析实验([code here](./ch6_support_vector_machine/6.2_SVM_test/))； - 支持向量机与BP网络/C4.5决策树对比实验（experiment of SVM/BP/C4.5）([code here](./ch6_support_vector_machine/6.3_SVM_compare/))； - 线性核SVM与LDA的关系（comparison of linear kernel SVM and LDA）； - 高斯核SVM与RBF网络的关系（comparison of RBF kernel SVM and RBF net）； - SVM对噪声敏感的原因（reason of noise-sensitive）； - SVR实验（experiment of SVR）([code here](./ch6_support_vector_machine/6.8_SVR_test/))； - 核对率回归设计（about kernel logistic regression - KLR）； ### [Ch5.Neural Networks (神经网络)](./ch5_neural_networks/) ### 练习包括(exercises include)： - 激活函数选择考虑（selection of activation function）； - Sigmoid激活函数与对率回归的联系（the relationships between Sigmoid() and Logistic Regression）； - BP算法推导（conduction of BP algorithm）； - 学习率分析（analysis of learning rate in NN training）； - 标准BP算法和累积BP算法对比实验（comparative experiment of BP）([code here](./ch5_neural_networks/5.5_BP/))； - BP算法改进（improvement of BP algorithm）([code here](./ch5_neural_networks/5.6_BP_improve/))； - RBF神经网络实现（implementation of RBF network）([code here](./ch5_neural_networks/5.7_RBF_BP/))； - SOM神经网络实验（experiment of SOM network）([code here](./ch5_neural_networks/5.8_SOM/))； - 卷积神经网络实验 - 字符识别（experiment of CNN on MNIST）([code here](./ch5_neural_networks/5.10_CNN/))； ### [Ch4.Decision Tree (决策树)](./ch4_decision_tree/) ### 练习包括(exercises include)： - 决策树划分选择准则； - 编程实现ID3算法（implementation of ID3）([code here](./ch4_decision_tree/4.3_ID3/))； - 编程实现CART算法与剪枝操作（implementation of CART and pruning）([code here](./ch4_decision_tree/4.4_CART/))； - 多变量决策树生成方式（multivariate decision tree）； - 非递归决策树生成方法（generation of decision - non-recursive approach using DFS/BFS） ### [Ch3.Linear Model (线性模型)](./ch3_linear_model/) ### 练习包括(exercises include)： - 分析偏置项b（bias terms）； - 证明对数似然是凸函数（convex functions）； - 编程实现对率回归（implementation of logistic regression）([code here](./ch3_linear_model/3.3_logistic_regression_watermelon/))； - 实验比较k-fold_CV和LOOCV（analysis of cross-validation）([code here](./ch3_linear_model/3.4_cross_validation/))； - 编程实现线性判别分析（implementation of LDA）（[code here](./ch3_linear_model/3.5_LDA/)）； - 线性判别分析的非线性拓展（nonlinear stretching of LDA）； - 最优ECOC编码方式； - 多分类到二分类分解时类别不平衡的考虑（class-imbalance）。 ### [Ch2.Model Evaluation and Selection (模型评估与选择)](./ch2_model_evaluation_and_selection/) ### 练习包括(exercises include)： - 分层抽样(stratified sampling)划分训练集与测试集； - 留一法(leave-one-out)与k-折交叉验证法(k-fold cross validation)比较; - F1值与BEP的关联; - TPR、FPR、P、R之间的关联; - AUC推导; - 错误率与ROC的关系 - ROC曲线与代价曲线(cost-curve)的对应关系; ### [Ch1.Introduction (绪论)](./ch1_introduction/) ### 练习包括(exercises include)： - 求版本空间(version space)； - 析合范式(disjunctive normal form)提升假设空间(hypothesis space)； - 噪声环境(noise)下归纳偏好(inductive bias)考虑；
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matched fp:02827537ffa8b71b, llm:Repository contains exercise answers to the book 'machine-learning' by Prof. Zhou Zhihua (description: 'Exercises answers to the book "machine-learning" written by Prof. Zhou Zhihua of Nanjing University').