Seiya Satoh

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In the parameter space of MLP(J), multilayer perceptron with J hidden units, there exist flat areas called singular regions created by applying reducibility mappings to the optimal solution of MLP( $$J-1$$ ). Since such singular regions cause serious stagnation of learning, a learning method to avoid singular regions has been desired. However, such avoiding(More)
Werner syndrome (WS) is an autosomal recessive progeroid disorder caused by mutations in RecQ DNA helicase. Ectopic soft tissue calcification is one of the well known symptoms in WS. However, the prevalence, clinical outcome, and mechanism of such calcification remain to be elucidated. The clinical features and mechanism of ectopic calcification were(More)
In the search space of a complex-valued multilayer perceptron (C-MLP) there exist flat areas called singular regions. Although singular regions cause serious stagnation of learning, there exist descending paths from the regions. Based on this observation, a completely new learning method for C-MLP, called C-SSF1.0, was proposed, making good use of singular(More)
In the search space of a complex-valued multilayer perceptron having J hidden units, C-MLP(J), there are singular regions, where the gradient is zero. Although singular regions cause serious stagnation of learning, there exist narrow descending paths from the regions. Based on this observation, a completely new learning method called C-SSF (complex(More)
In a search space of a multilayer perceptron having J hidden units, MLP(J), there exist flat areas called singular regions. Since singular regions cause serious stagnation of learning, a learning method to avoid them was once proposed, but was not guaranteed to find excellent solutions. Recently, SSF1.2 was proposed which utilizes singular regions to stably(More)
A complex-valued multilayer perceptron has the capability to represent complicated periodicity. We employ a very powerful learning method called C-SSF for learning a complex-valued multilayer perceptron. C-SSF finds a series of excellent solutions through successive learning. In deterministic chaos, long-term prediction is considered impossible. We apply(More)