For hendes ansigt skinnede som solen

Merge SOM for temporal data

The recent merging self-organizing map (MSOM) for unsupervised sequence processing constitutes a fast, intuitive, and powerful unsupervised learning model. In this paper, we investigate its theoretical and practical properties. Particular focus is put on the context established by the self-organizing MSOM, and theoretic results on the representation capabilities and the MSOM training dynamic ar...

Som Input

Using Som and Lvq for Hmm Training 1.1.1 the Segmental Som Training

1.1 New training methods for the HMMs The training of the context-independent phoneme models for a minimal recognition error rate is diicult, because the variability of the phonemes in diierent conditions and contexts is substantial and the output densities of diierent phonemes do also overlap. A structure that can automatically adapt to all the complicated density functions, has a vast number ...

New Usage of SOM for Genetic Algorithms

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantizations, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve mo...

SOM-Based R*-tree for Similarity Retrieval

Feature-based similarity retrieval has become an iniportant research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e.g., documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors. A feature vector is a vector that represents ...