We turn next to the task of finding a weight vector w which minimizes the chosen function E(w). Because there is clearly no hope of finding an anlytical solution to the equation ∂E(w)=0, we resort to iterative numerical procedures. On-line gradient d
物体识别:SIFT 特征: 人脸识别:LBP 特征: 行人检测:HOG 特征: 0. 常见手工设计的低级别特征 manually designed low-level features 语音:高斯混合模型和隐马尔可夫模型: Gabor features for : texture classification Local Binary Patterns (LBP) for: face classification. SIFT and HOG features for: object recogn
警告:本文为小白入门学习笔记 数据连接: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearning&doc=exercises/ex2/ex2.html 数据集是(x(i),y(i)) x = load('ex2x.dat'); y = load('ex2y.dat'); plot(x, y, 'o'); 假设函数(hypothesis function): 接下来用矩阵的形式表示x: m