in the two dimensional output space. The unsupervised process leads to the self organization of modeling with no previous knowledge of what is being modeled and therefore it does not model a predetermined environment. Taking the above into account feature selection was performed by analyzing the contributions of different sensor based features towards tool wear classification. It was found that...

In this paper, it is shown that the Feature-Extracting Bidirectional Associative Memory (FEBAM) can encompass competitive model features based on winner-take-all, kwinners-take-all and self-organizing feature map properties. The modified model achieves perceptual multidimensional feature extraction, cluster-based category formation through simultaneous creation of prototype/exemplar memories, a...

In this paper we present a method for document organization and retrieval based on statistical language modeling.The proposed method, which is based on the vector model, uses nonlinear interpolation to provide more accurate statistical estimators of the conditional probabilities employed for encoding the context of each word. An information retrieval system is built using the self-organizing ma...

In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that ...

In this paper we aim to detect some aspects of adjectival meanings. Concepts of adjectives are distributed by SOM (SelfOrganizing map) whose feature vectors are calculated by MI (Mutual Information). For the SOM obtained, we make tight clusters from map nodes, calculated by cosine. In addition, the number of tight clusters obtained by cosine was increased using map nodes and Japanese thesaurus....

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. They represent powerful data analysis tools applied in many different areas including areas such as biomedicine, bioinformatics, proteomics, and astrophysics. We maintain a data analysis package in R based on self-organizing maps. The package supports efficient, statistical measures that enable the u...

The self-organizing map (SOM) is a method that represents statistical data sets in an ordered fashion, as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed. As a case study that instructs how to use the SOM to compare states of economic systems, the standard of living of different countries is analyzed using the SOM. Based on a...

In this study, we try to implant chaotic features into the learning algorithm of self-organizing map. We call this concept as Chaotic SOM (CHAOSOM). As a first step to realize CHAOSOM, we consider the case that learning rate and neighboring coefficient of SOM are refreshed by chaotic pulses generated by the Hodgkin-Huxley equation. We apply the CHAOSOM to solve a traveling salesman problem and ...