In this paper, we discuss the use of Self Organizing Maps (SOM) for character and word clustering. The SOM is a particular kind of artificial neural network that computes an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning and SOM in the field of Document Image Analysis we describe our r...

Keywords: Topology preserving Self-organizing map Growing cell structures Visualization methods Delaunay triangulation The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsuper...

The Self-Organizing Map (SOM ) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper we describe Fast Learning SOM (FLSOM ) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that ...

The Self-Organizing Map (SOM) is one of the most popular neural network methods. It is a powerful tool in visualization and analysis of high-dimensional data in various engineering applications. The SOM maps the data on a two-dimensional grid which may be used as a base for various kinds of visual approaches for clustering, correlation and novelty detection. In this chapter, we present novel me...

The self-organizing map (SOM) is a common methodology used to capture and represent data patterns and increasingly playing a significant role in the development of neural networks. The primary objective of an SOM is to determine an approximate representation of data with an unknown probability distribution, from a multi-dimensional input space, using a lower dimensional neural network. The appr...

Interpretation of models induced by artiicial neural networks is often a diicult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that ooers possibilities to overcome this diiculty. It is shown that networks trained with bp-som show interesting regularities, in that hidden-unit activations become restricted to discrete values, and th...

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Robotics research devotes considerable attention to path nding. This is the problem of moving a robot from a starting position to a goal avoiding obstacles. Also, the robot path must be short and smooth. Traditionally, path nders are either model-based or sensor-based. While model-based systems address the path nding problem globally using a representation of the workspace, sensor-based systems...

Recently, security of the computer systems becomes an important problem. Almost all computers use the password mechanism for the user authentication. But password mechanism has many issues. In this paper, we propose a kind of biometrics authentication method using the combinations of key stroke timings and pen calligraphy. For this method, selection of the phrase is important. We analyzed the k...

The goal of the article is to presents intrusion detections systems and design architecture of intrusion detection based on neural network self organizing map. In the report is described base problematic of neural network and intrusion detection system. The article further deals with specific design of intrusion detection architecture based on user anomaly behavior. A core of the designed archi...