We describe the MusicMiner system for organizing large collections of music with databionic mining techniques. Visualization based on perceptually motivated audio features and Emergent Self-Organizing Maps enables the unsupervised discovery of timbrally consistent clusters that may or may not correspond to musical genres and artists. We demonstrate the visualization capabilities of the U-Map. A...

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the tw...

A substantial fraction of all network tra c today comes from applications in which clients retrieve objects from servers. The caching of objects in locations \close" to clients is an important technique for reducing both network tra c and response time for such applications. In this paper we consider the bene ts of associating caches with switching nodes throughout the network, rather than in a...

Motion planning in the configuration space (C-space) induces benefits, such as smooth trajectories. It becomes more complex degrees of freedom (DOF) increase. This is due to direct relation between dimensionality search and DOF. Self-organizing neural networks (SONN) with their famous candidate, Self-Organizing Map, have been proven be useful tools for C-space reduction while preserving its und...

We ooer three algorithms for the generation of topographic mappings to the practitioner of unsupervised data analysis. The algorithms are each based on the minimization of a cost function which is performed using an EM algorithm and de-terministic annealing. The soft topographic vector quantization algorithm (STVQ) { like the original Self-Organizing Map (SOM) { provides a tool for the creation...

Q-learning as well as other learning paradigms depend strongly on the representation of the underlying state space. As a special case of the hidden state problem we investigate the e ect of a self-organizing discretization of the state space in a simple control problem. We apply the neural gas algorithm with adaptation of learning rate and neighborhood range to a simulated cart-pole problem. Th...