13 research outputs found
Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning
We propose an optimization method of mutual learning which converges into the
identical state of optimum ensemble learning within the framework of on-line
learning, and have analyzed its asymptotic property through the statistical
mechanics method.The proposed model consists of two learning steps: two
students independently learn from a teacher, and then the students learn from
each other through the mutual learning. In mutual learning, students learn from
each other and the generalization error is improved even if the teacher has not
taken part in the mutual learning. However, in the case of different initial
overlaps(direction cosine) between teacher and students, a student with a
larger initial overlap tends to have a larger generalization error than that of
before the mutual learning. To overcome this problem, our proposed optimization
method of mutual learning optimizes the step sizes of two students to minimize
the asymptotic property of the generalization error. Consequently, the
optimized mutual learning converges to a generalization error identical to that
of the optimal ensemble learning. In addition, we show the relationship between
the optimum step size of the mutual learning and the integration mechanism of
the ensemble learning.Comment: 13 pages, 3 figures, submitted to Journal of Physical Society of
Japa
Public channel cryptography by synchronization of neural networks and chaotic maps
Two different kinds of synchronization have been applied to cryptography:
Synchronization of chaotic maps by one common external signal and
synchronization of neural networks by mutual learning. By combining these two
mechanisms, where the external signal to the chaotic maps is synchronized by
the nets, we construct a hybrid network which allows a secure generation of
secret encryption keys over a public channel. The security with respect to
attacks, recently proposed by Shamir et al, is increased by chaotic
synchronization.Comment: 4 page
Synchronization of random walks with reflecting boundaries
Reflecting boundary conditions cause two one-dimensional random walks to
synchronize if a common direction is chosen in each step. The mean
synchronization time and its standard deviation are calculated analytically.
Both quantities are found to increase proportional to the square of the system
size. Additionally, the probability of synchronization in a given step is
analyzed, which converges to a geometric distribution for long synchronization
times. From this asymptotic behavior the number of steps required to
synchronize an ensemble of independent random walk pairs is deduced. Here the
synchronization time increases with the logarithm of the ensemble size. The
results of this model are compared to those observed in neural synchronization.Comment: 10 pages, 7 figures; introduction changed, typos correcte