移动教师绕真教师在线学习分析
Analysis of on-line learning when a moving teacher goes around a true teacher
摘要：
在在线学习的框架中，由于教师和学习机之间的结构或输出函数的差异或由于噪声，学习机可能在教师周围移动。分析了一个新学生在移动机器指导下的泛化性能。利用统计力学对一个由真实教师、运动教师和学生组成的模型进行了分析。已经证明，学生的泛化错误可以小于移动的教师，即使学生只使用移动的教师的例子。
英文摘要：
In the framework of on-line learning, a learning machine might move around a teacher due to the differences in structures or output functions between the teacher and the learning machine or due to noises. The generalization performance of a new student supervised by a moving machine has been analyzed. A model composed of a true teacher, a moving teacher and a student that are all linear perceptrons with noises has been treated analytically using statistical mechanics. It has been proven that the generalization errors of a student can be smaller than that of a moving teacher, even if the student only uses examples from the moving teacher.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
