Search results for "self-organizing"
showing 10 items of 88 documents
Hierarchies of Self-Organizing Maps for action recognition
2016
We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-laye…
Order Parameters for Self-Organizing Maps
1998
We introduce and discuss different approaches to construct order parameters for Kohonen’s Self-Organizing Maps. As one approach the notion of an order parameter in the sense of Haken’s synergetics is studied and contrasted with organization measures using SOM structure information.
Complexity Selection of the Self-Organizing Map
2002
This paper describes how the complexity of the Self-Organizing Map can be selected using the Minimum Message Length principle. The use of the method in textual data analysis is also demonstrated.
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …
Visual Data Mining in Physiotherapy Using Self-Organizing Maps
2013
The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of…
A New SOM Initialization Algorithm for Nonvectorial Data
2008
Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms…
Analysis of Multi-Choice Questionnaires through Self-Organizing Maps
1998
This paper describes how Self-Organizing Maps can be used to analyse multi-choice gallups. In this method, the use of a single SOM for all available data is replaced with the use of multiple SOMs trained with subsets of gallup questions. The subgroupings located from these maps are then used to train a new concluding SOM that is more readable than any single SOM analysis would be.
RECURRENT SELF-ORGANIZATION OF SENSORY SIGNALS IN THE AUDITORY DOMAIN
2008
In this study, a psychoacoustical and connectionist modeling framework is proposed for the investigation of musical cognition. It is suggested that music perception involves the manipulation of 1) sensory representations that have correlations with psychoacoustical features of the stimulus, and 2) abstract representations of the statistical regularities underlying a particular musical syntax. In the implicit learning domain, sensory features have been shown to interact with the processes involved in the extraction of the regularities governing musical events combinations in a stream [e.g., 1]. Furthermore, in a more ecological context, it is well known that traditional Western tonal system …
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
2004
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…
Mapping Economic Activity in the European Union: Do Ownership, Industry and Location Matter?
2020
The paper proposes a new method for analysing the structure and dynamics of economic activity undertaken by locally owned and foreign-owned companies within the European Union. We employ an unsupervised learning algorithm that generates a neural network depicted on Kohonen maps and offering a clustering of companies with a different ownership (local and foreign) from various industries and countries of the European Union during 2009–2016. The research methodology, based on a self-organizing map (SOM) algorithm, belongs to a class of neural networks trained to organize data so that unknown patterns may be discovered, thus leading to results that cannot be attained by more traditional cluster…