Soil organic matter (SOM) is known to play vital roles in the maintenance and improvement of many soil properties and processes. These roles, which largely influence soil functions, are a pool of specific contributions of different components of SOM. The soil functions, in turn, normally define the level of soil degradation, viewed as quantifiable temporal changes in a soil that impairs its qua...

1 ABSTRACT Colorectal tumors are responsible for more than 600 000 deaths per year worldwide and thereby constitute the second most common cause of cancer related mortality. Early detection is related to improved prognosis and identification of genetic biomarkers would meliorate available diagnostic tools. Existing tumor classification systems lack precise monitoring within individual tumor sta...

For unsupervised sequence processing, standard self organizing maps (SOM) can be naturally extended by recurrent connections and explicit context representations. Known models are the temporal Kohonen map (TKM), recursive SOM, SOM for structured data (SOMSD), and HSOM for sequences (HSOM-S). We discuss and compare the capabilities of exemplary approaches to store different types of sequences. A...

PROPRE is a generic and semi-supervised neural learning paradigm that extracts meaningful concepts of multimodal data flows based on predictability across modalities. It consists on the combination of two computational paradigms. First, a topological projection of each data flow on a self-organizing map (SOM) to reduce input dimension. Second, each SOM activity is used to predict activities in ...

The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning effi...

. This paper devoted to an iris recognition system (IRS) designed using 2D-Discrete Cosine Transform (DCT) features and Self Organizing Map (SOM) and Radial Basis Function (RBF) which are an Artificial Neural Network (ANN) used as classifier. DCT is used for feature extraction to capture essential details. SOM and RBF are applied for classification with different functional paradigms. With resp...

The Self-Organizing Map (SOM) is a popular algorithm to analyze the structure of a dataset. However, some topological constraints of the SOM are fixed before the learning and may not be relevant regarding to the data structure. In this paper we propose to improve the SOM performance with a new algorithm which learn the topological constraints of the map using data structure information. Experim...

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algo...