This dissertation studies the problem of clustering objects represented by relational data. This is a pertinent problem as many real-world data sets can only be represented by relational data for which object-based clustering algorithms are not designed. Relational data are encountered in many fields including biology, management, industrial engineering, and social sciences. Unlike numerical ob...

Abstract We study asset pricing implications of a revealing and tractable formulation smooth ambiguity investor preferences in continuous-time environment. Investors do not observe hidden Markov state instead make inferences about this using past data. show that distribution alters decisions equilibrium prices. Our allows us to apply recursive filtering Hamilton–Jacobi–Bellman methods solve the...

• An active recursive inference method based on Rényi information measures. Posterior probability changes can enhance both query optimization and stopping criterion in the RBI process. Using entropy α -divergence unifies multi-objective problem. The proposed unified framework enhances speed accuracy, presence of an adversarial prior. Recursive Bayesian (RBI) provides optimal latent variable est...

We review a recent extension of the self-organizing map (SOM) for temporal structures with a simple recurrent dynamics leading to sparse representations, which allows an efficient training and a combination with arbitrary lattice structures. We discuss its practical applicability and its theoretical properties. Afterwards, we put the approach into a general framework of recurrent unsupervised m...

We review a recent extension of the self-organizing map (SOM) for temporal structures with a simple recurrent dynamics leading to sparse representations, which allows an efficient training and a combination with arbitrary lattice structures. We discuss its practical applicability and its theoretical properties. Afterwards, we put the approach into a general framework of recurrent unsupervised m...

This paper discusses intuitive and eficient ways to model and control the dynamics of highly redundant branching mechanisms using the operational space formulation. As the complexity of mechanisms increases, their modeling and control become increasingly d i f i cult. The operational space formulation provides a natural framework for these problems since its basic structure provides dynamic dec...

We present a discussion of three adaptive filtering algorithms well known for their one-step termination property, in terms of their relationship with the minimal residual method. These algorithms are the normalized least mean square (NLMS), Affine Projection algorithm (APA) and the recursive least squares algorithm (RLS). The NLMS is shown to be a result of the orthogonality condition imposed ...