It is expected that the agricultural intensification occurred in recent decades in the Argentine Rolling Pampa significantly alters the SOM reserves. Therefore, it is necessary to identify soil organic carbon (C) and nitrogen (N) fractions to understand the functionality and stabilization of these reserves. Our objectives were to study the NT effect in two crop rotations, corn-double cropped wh...

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst’s expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial di...

—This paper presents a constrained self-organizing map (SOM) model for the visualization and reconstruction of the human brain lateral ventricle. The SOM model is a widely used method to approximate large and complex high dimensional data and reduce the data dimension for advanced applications. In our applications, the SOM model is used to deform a spherical network field to a 3D crooked brain ...

BACKGROUND Sphincter of Oddi manometry (SOM) is recommended in the evaluation of suspected Type II sphincter of Oddi dysfunction (SOD2), though its utility is uncertain. Little is known about the practice of expert endoscopists in the United States regarding SOD2. METHODS An anonymous electronic survey was distributed to 128 expert biliary endoscopists identified from U.S. advanced endoscopy ...

This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning a...

As a neural approach, Kohonen's Self-Organizing Map (SOM) has not been explored as thoroughly as the MLP, especially for the soft classification. In this paper, we propose two non-parametric algorithms for the SOM to provide soft classification outputs. These algorithms, which are labeling-frequency-based and are called SOM Commitment (SOMC) and SOM Typicality (SOM-T), expressing in the first c...

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships wit...

The Self-Organizing Map (SOM) is an artificial neural network model based on unsupervised learning. In this paper, the use of the SOM in natural language processing is considered. The main emphasis is on natural features of natural language including contextuality of interpretation, and the communicative and social aspects of natural language learning and usage. The SOM is introduced as a gener...

We have experimented with a bio-inspired selforganizing texture and hardness perception system which automatically learns to associate the representations of the two submodalities with each other. To this end we have developed a microphone based texture sensor and a hardness sensor that measures the compression of the material at a constant pressure. The system is based on a novel variant of th...

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the recent years, several variations on the Self Organizing Maps (SOM) were introduced in the literature. In this paper, the toroidal Emergent SOM tool and the spherical SOM are used to analyze a text corpus consisting of police reports of all violent incidents that occurred during the ...