机器学习练习文件.rar

[{"title":"（ 21 个子文件 3.89MB ） 机器学习练习文件.rar","children":[{"title":"机器学习练习文件","children":[{"title":"决策树与随机森林.ipynb <span style='color:#111;'> 242.77KB </span>","children":null,"spread":false},{"title":"KDE示例：球形空间的KDE.ipynb <span style='color:#111;'> 86.43KB </span>","children":null,"spread":false},{"title":"随机森林案例：识别手写数字.ipynb <span style='color:#111;'> 69.51KB </span>","children":null,"spread":false},{"title":"高斯混合模型（GMM）.ipynb <span style='color:#111;'> 503.45KB </span>","children":null,"spread":false},{"title":"支持向量机.ipynb <span style='color:#111;'> 383.24KB </span>","children":null,"spread":false},{"title":"GMM示例：用GMM生成新的数据.ipynb <span style='color:#111;'> 74.61KB </span>","children":null,"spread":false},{"title":"机器学习基础.ipynb <span style='color:#111;'> 96.21KB </span>","children":null,"spread":false},{"title":"k-means聚类案例.ipynb <span style='color:#111;'> 1.14MB </span>","children":null,"spread":false},{"title":"流形学习示例：用Isomap处理人脸数据.ipynb <span style='color:#111;'> 2.36MB </span>","children":null,"spread":false},{"title":"流形学习.ipynb <span style='color:#111;'> 353.38KB </span>","children":null,"spread":false},{"title":"SVM案例：人脸识别.ipynb <span style='color:#111;'> 162.90KB </span>","children":null,"spread":false},{"title":"线性回归.ipynb <span style='color:#111;'> 228.17KB </span>","children":null,"spread":false},{"title":"主成分分析案例：特征脸.ipynb <span style='color:#111;'> 270.41KB </span>","children":null,"spread":false},{"title":"KDE核密度图.ipynb <span style='color:#111;'> 88.20KB </span>","children":null,"spread":false},{"title":"k-means聚类.ipynb <span style='color:#111;'> 275.72KB </span>","children":null,"spread":false},{"title":"KDE示例：不是很朴素的贝叶斯.ipynb <span style='color:#111;'> 2.66MB </span>","children":null,"spread":false},{"title":"主成分分析.ipynb <span style='color:#111;'> 247.77KB </span>","children":null,"spread":false},{"title":"手写数字探索.ipynb <span style='color:#111;'> 173.83KB </span>","children":null,"spread":false},{"title":"流形学习示例：手写数字的可视化结构.ipynb <span style='color:#111;'> 150.24KB </span>","children":null,"spread":false},{"title":"方向梯度直方图（HOG）——图像特征提取技术.ipynb <span style='color:#111;'> 442.33KB </span>","children":null,"spread":false},{"title":"朴素贝叶斯分类.ipynb <span style='color:#111;'> 197.87KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]