The EM algorithm is an iterative statistical technique of using the conditional expectation, and the em algorithm is a geometrical one given by information ...
The EM algorithm is an iterative statistical technique of using the conditional expectation, and the em algorithm is a geometrical one given by information ...
The other is the em algorithm (e- and m-geodesic projections) orig- inated from information geometry (Amari 1991; Amari et al. 1992; Byrne. 1992) and applied to ...
To realize an input-output relation given by noise-contaminated examples, it is effective to use a stochastic model of neural networks.
Abstract: Hidden units play an important role in neural networks, although their activation values are unknown in many learning situations. The EM algorithm ...
Sep 3, 2022 · In this paper, we introduce the em algorithm, an information geometric formulation of the EM algorithm, and its extensions and applications to ...
In this paper, we introduce the em algorithm, an information geometric formulation of the EM algorithm, and its extensions and applications to various problems.
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Jan 1, 1995 · The EM algorithm (statistical algorithm) and the em algorithm (information-geometric one) have been proposed so far in this connection, and the ...
We give a uuified framework of information geometry (Amari, 1985) to elucidate the ahove two algorithms. A mOl"(' detailed paper will appear in. Alllari [1994].
The em-algorithm iteratively minimizes the Kullback-Leibler divergence in the manifold of neural networks. These two algorithms are equivalent in most cases.