Statistical mechanics of on-line ensemble teacher learning through a novel perceptron learning rule ,
Statistical mechanics of on-line ensemble teacher learning through a novel perceptron learning rule ,
复制标题
通过新颖的感知器学习规则进行在线集成教师学习的统计机制，
DOI：
10.1143/jpsj.81.064002
复制
发表时间：
2012
影响因子：
1.7
通讯作者：
S.,
中科院分区：
文献类型：
--
作者：
Hara;K. and Miyoshi;S.,
在合群教师学习中，合群教师对真正的教师只有不确定的信息，这些信息是由无限数量的合群教师组成的合群提供的，这些合群教师的多样性足够丰富。在这种学习中，学生从一个集合老师那里学习，这个集合老师是从许多集合老师中迭代随机选择的。集体教师学习的一个有趣点是学生通过向集体教师学习而接近真正的教师的渐近行为。在学习中运用Hebbian学习规则，提高了学生的学习成绩。然而，感知器学习规则并不能提高学生的成绩。另一方面，我们提出了一个带边际的感知器学习规则。这个学习规则和感知器的学习规则是一样的当边缘为零和Hebbian的学习规则是一样的当边缘为无穷大。因此，该规则通过边距的大小将感知器学习规则和Hebbian学习规则连续连接起来。利用该规则，我们研究了从感知器学习规则到Hebbian学习规则的学习行为的变化，并考虑了几种边缘大小。从结果中，我们发现通过设置κ>的裕度，当使用更大的裕度κ时，集成效应出现并变得显著。
In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point of ensemble teacher learning is the asymptotic behavior of the student to approach the true teacher by learning from ensemble teachers. The student performance is improved by using the Hebbian learning rule in the learning. However, the perceptron learning rule cannot improve the student performance. On the other hand, we proposed a perceptron learning rule with a margin. This learning rule is identical to the perceptron learning rule when the margin is zero and identical to the Hebbian learning rule when the margin is infinity. Thus, this rule connects the perceptron learning rule and the Hebbian learning rule continuously through the size of the margin. Using this rule, we study changes in the learning behavior from the perceptron learning rule to the Hebbian learning rule by considering several margin sizes. From the results, we show that by setting a margin of κ>0, the effect of an ensemble appears and becomes significant when a larger margin κ is used.