朴素贝叶斯
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朴素贝叶斯（naive Bayes ）法是基于贝叶斯定理与特征条件独立假设的分类方法①。对于给定的训练数据集，首先基于特征条件独立假设学习输入输出的联合概率分布；然后基于此模型，对给定的输入 x，利用贝叶斯定理求出后验概率最大的输出 y。
激活函数
Relu
Sigmoid
1/(1+e^(-z))
Tanh
双曲正切
LeakyRelu
Batch Normalization
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全连接中
将批量归 ⼀ 化层置于全连接层中的仿射变换和激活函数之间。设全连接层的输⼊为 u，权重参数和偏差参数分别为 W和 b，激活函数为 ϕ。设批量归⼀化的运算符为BN 。那么，使⽤批量归⼀化的全连接层的输出为 ϕ(BN (x))
卷积中
批量归 ⼀ 化发 ⽣ 在卷积计算之后、应⽤激活函数之前。如果卷积计算输出多个通道，我们需要对这些通道的输出分别做批量归⼀化，且每个通道都拥有独⽴的拉伸和偏移参数，并均为标量。
K-means
输入：样本集D，簇的数目k，最大迭代次数N；
输出：簇划分（k个簇，使平方误差最小）；
算法步骤：
（1）为每个聚类选择一个初始聚类中心；
（2）将样本集按照最小距离原则分配到最邻近聚类；
（3）使用每个聚类的样本均值更新聚类中心；
（4）重复步骤（2）、（3），直到聚类中心不再发生变化；
（5）输出最终的聚类中心和k个簇划分。
迭代方法
梯度下降
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Adagrad
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经典评价指标
Recall, precision
Recall = TPR, Recall表示实际为正的样本被判断为正样本的比例;
precision = TP/(TP+FP) 表示预测为正的样本中，实际的正样本的数量。
F1-score
F1 = 2 * P * R / (P+R)
Averaged balanced score
参考：https://www.jianshu.com/p/2eb432f31a6b
Original: https://blog.csdn.net/qq_36993032/article/details/123979054
Author: Lloaded%356
Title: 机器学习/深度学习准备资料（待更新）
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