我正在研究机器学习中的感知器算法。到目前为止,我了解了关于感知器的以下内容
1)It's a supervised learning technique
2)It tries to create a hyper plane that linearly separates the class
labels ,which is when the perceptron converges
3)if the predicted output and the obtained output from the algorithm
doesnot match it adjusts it's weight vector and bias.
但是,如果感知器没有实现收敛,我无法理解权重向量会发生什么?算法是否不断
更新权重向量?