1 citations · 1 across the 3 of their papers we have counts for
4 papers
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional dist…
Full-Span Log-Linear Model and Fast Learning Algorithm
Kazuya Takabatake, Shotaro Akaho
The full-span log-linear(FSLL) model introduced in this paper is considered an -th order Boltzmann machine, where is the number of all variables in the target system. Let $X…
Reconsidering Dependency Networks from an Information Geometry Perspective
Kazuya Takabatake, Shotaro Akaho
Dependency networks (Heckerman et al., 2000) are potential probabilistic graphical models for systems comprising a large number of variables. Like Bayesian networks, the structure…
Constraint-free Graphical Model with Fast Learning Algorithm
Kazuya Takabatake, Shotaro Akaho
In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given…