svm算法手写matlab代码-Machine-Learning-Code:此存储库是一些代码，其中打包了机器学习中的一些常用方法

[{"title":"（ 34 个子文件 63KB ） svm算法手写matlab代码-Machine-Learning-Code:此存储库是一些代码，其中打包了机器学习中的一些常用方法","children":[{"title":"Machine-Learning-Code-master","children":[{"title":"ann_by_matlab","children":[{"title":"softmax.m <span style='color:#111;'> 88B </span>","children":null,"spread":false},{"title":"read_mnist_labels.m <span style='color:#111;'> 298B </span>","children":null,"spread":false},{"title":"ANN.m <span style='color:#111;'> 2.86KB </span>","children":null,"spread":false},{"title":"readint32.m <span style='color:#111;'> 200B </span>","children":null,"spread":false},{"title":"read_mnist_images.m <span style='color:#111;'> 486B </span>","children":null,"spread":false}],"spread":true},{"title":"KMeans++","children":[{"title":"KMeans.py <span style='color:#111;'> 5.64KB </span>","children":null,"spread":false}],"spread":true},{"title":"LHalf","children":[{"title":"LHalf.py <span style='color:#111;'> 5.56KB </span>","children":null,"spread":false},{"title":"energy_predict.py <span style='color:#111;'> 3.13KB </span>","children":null,"spread":false}],"spread":true},{"title":"linear_model","children":[{"title":"lasso_test.py <span style='color:#111;'> 1.32KB </span>","children":null,"spread":false},{"title":"coordinate_descent.R <span style='color:#111;'> 2.54KB </span>","children":null,"spread":false},{"title":"Ridge.R <span style='color:#111;'> 1014B </span>","children":null,"spread":false},{"title":"Multiple_test.py <span style='color:#111;'> 5.65KB </span>","children":null,"spread":false},{"title":"Multiple_linear_regression.py <span style='color:#111;'> 23.69KB </span>","children":null,"spread":false}],"spread":true},{"title":"SVM","children":[{"title":"support_vector_machine.py <span style='color:#111;'> 18.38KB </span>","children":null,"spread":false}],"spread":true},{"title":"xgboost","children":[{"title":"BaseTree.py <span style='color:#111;'> 12.73KB </span>","children":null,"spread":false},{"title":"LossFunc.py <span style='color:#111;'> 676B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 355B </span>","children":null,"spread":false},{"title":"XGBModel.py <span style='color:#111;'> 10.02KB </span>","children":null,"spread":false},{"title":"XGBoost_test.py <span style='color:#111;'> 1.52KB </span>","children":null,"spread":false},{"title":"LeastSquareLoss.py <span style='color:#111;'> 245B </span>","children":null,"spread":false}],"spread":true},{"title":"DecisionTree","children":[{"title":"DecisionTree_test.py <span style='color:#111;'> 2.84KB </span>","children":null,"spread":false},{"title":"Cart.py <span style='color:#111;'> 10.89KB </span>","children":null,"spread":false},{"title":"CartTest.py <span style='color:#111;'> 1.65KB </span>","children":null,"spread":false},{"title":"treePlotter.py <span style='color:#111;'> 2.68KB </span>","children":null,"spread":false},{"title":"DecisionTree.py <span style='color:#111;'> 13.37KB </span>","children":null,"spread":false}],"spread":true},{"title":"NaiveBayes","children":[{"title":"naive_bayes.py <span style='color:#111;'> 12.03KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.20KB </span>","children":null,"spread":false},{"title":"naive_bayes_test.py <span style='color:#111;'> 2.13KB </span>","children":null,"spread":false}],"spread":true},{"title":"rejection_sampling","children":[{"title":"rejection_sampling.py <span style='color:#111;'> 6.35KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 3.27KB </span>","children":null,"spread":false},{"title":"Gan","children":[{"title":"gan.py <span style='color:#111;'> 7.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"CNN","children":[{"title":"captcha_pic_builder.py <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"identification_codes.py <span style='color:#111;'> 7.57KB </span>","children":null,"spread":false},{"title":"input_data.py <span style='color:#111;'> 5.83KB </span>","children":null,"spread":false}],"spread":true}],"spread":false}],"spread":true}]