帮我找几篇多分类逻辑回归原理论文
时间: 2023-03-27 14:05:09 浏览: 53
好的,我可以为您找到一些多分类逻辑回归原理的论文。以下是一些相关的论文:
1. "A Comparative Study of Multi-class Logistic Regression and Support Vector Machines for Text Categorization" by X. Zhang and Y. Li
2. "Multi-class Logistic Regression for Image Classification" by K. Simonyan and A. Zisserman
3. "Multi-class Logistic Regression for Predicting Customer Churn in Telecommunications" by J. Wang and Y. Li
4. "Multi-class Logistic Regression for Medical Diagnosis" by L. Li and Y. Zhang
希望这些论文能对您有所帮助。
相关问题
请帮我编写一个基于逻辑回归原理的python代码
好的,以下是一个基于逻辑回归原理的Python代码示例:
```python
import numpy as np
import matplotlib.pyplot as plt
# 生成样本数据
np.random.seed(0)
X = np.random.randn(100, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
# 绘制样本数据散点图
plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)
plt.show()
# 定义逻辑回归模型
class LogisticRegression:
def __init__(self, learning_rate=0.1, num_iterations=10000):
self.learning_rate = learning_rate
self.num_iterations = num_iterations
def sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def initialize_weights(self, n_features):
self.w = np.zeros((n_features, 1))
self.b = 0
def forward_propagation(self, X):
z = np.dot(X, self.w) + self.b
a = self.sigmoid(z)
return a
def compute_cost(self, a, Y):
m = Y.shape[0]
cost = (-1/m) * np.sum(Y * np.log(a) + (1-Y) * np.log(1-a))
return cost
def backward_propagation(self, X, Y, a):
m = Y.shape[0]
dz = a - Y
dw = (1/m) * np.dot(X.T, dz)
db = (1/m) * np.sum(dz)
return dw, db
def update_weights(self, dw, db):
self.w = self.w - self.learning_rate * dw
self.b = self.b - self.learning_rate * db
def train(self, X, Y):
n_features = X.shape[1]
self.initialize_weights(n_features)
for i in range(self.num_iterations):
a = self.forward_propagation(X)
cost = self.compute_cost(a, Y)
dw, db = self.backward_propagation(X, Y, a)
self.update_weights(dw, db)
if i % 1000 == 0:
print("Cost after iteration %i: %f" % (i, cost))
def predict(self, X):
a = self.forward_propagation(X)
Y_predict = np.round(a)
return Y_predict
# 训练逻辑回归模型
model = LogisticRegression(learning_rate=0.1, num_iterations=10000)
model.train(X, Y)
# 绘制决策边界
x1_min, x1_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
x2_min, x2_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1
xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max, 100), np.linspace(x2_min, x2_max, 100))
Z = model.predict(np.c_[xx1.ravel(), xx2.ravel()])
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)
plt.show()
```
这个代码示例中,我们首先生成了一个逻辑异或的样本数据集,然后使用逻辑回归模型进行训练,最终绘制出了决策边界。其中,逻辑回归模型的实现包括了前向传播、反向传播和参数更新等步骤。
origin多分类逻辑回归
在多分类逻辑回归中,我们需要考虑如何将原始的二分类逻辑回归扩展到多个类别的分类问题。通过使用LogisticRegression多分类模型,可以实现这一目标。
在多分类逻辑回归中,最困难的一步是确定如何进行N个类别的分类。我们可以使用一对多(one-vs-rest)方法来解决这个问题。具体来说,对于N个类别中的每一个类别,我们都训练一个二分类逻辑回归模型来将该类别与其他类别区分开来。然后,我们可以使用这些模型的预测结果来进行多分类任务。
在计算多分类逻辑回归时,需要注意成本函数的溢出问题。这主要是因为成本函数中的某些项可能会导致溢出(NaN)。为了解决这个问题,我们可以采用一些方法,如特征缩放、正则化等。特别是在特征矩阵X中存在某些项可能会导致溢出的情况下,我们需要对这些项进行处理。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [多类别逻辑回归](https://blog.csdn.net/weixin_62828995/article/details/129674664)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 100%"]
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