Automatically inferring ongoing activities is to enable the early recognition of unfinished activities, which is quite meaningful for applications, such as online human-machine interaction and security monitoring. Stateof-the-art methods use the spatio-temporal interest point (STIP) based features as the low-level video description to handle complex scenes [1, 2, 3]. While the existing problem ...

Satellite constellation design is one kind of typical Multiobjective Optimization Problem (MOP). In this paper, aim at feature of high-dimensional decision space, an model-based multiobjective Evolutionary Algorithm(EA) via Self Organization Feature Map(SOM) is put forward for reducing decision space dimension of satellite constellation design: internal topology of input training set in the pop...

An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user's actions. A neural network model based on self organization is proposed for detecting intrusions. The selforganizing map (SOM) has shown to be s...

The continuous growth in the size and use of the Internet is creating difficulties in the search for information. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this paper, we evaluate the feasibility of using a self-organizing map (SOM) to mine web log data and provide a visual tool to assist user navigation. ...

The extend of the internet across world has increased cyber-attacks and threats. One most significant threats includes denial-of-service (DoS) which causes server or network not to be able serve. This attack can done by distributed nodes in as if collaborated. is called (DDoS). There offered a novel architecture for future networks make them more agile, programmable flexible. software defined (...

Image classification is an important topic in digital image processing, and it could be solved by pattern recognition methods. This paper is a survey based on Self Organising Maps used as a supervised algorithm for image classification. It is observed that SOM can be used as a supervised method, and can have better advantages: better predictions, easier to interpret and better stability. Keywor...

We do research on moving object classification in traffic video. Our aim is to classify the moving objects into pedestrians, bicycles and vehicles. Due to the advantage of self-organizing feature map (SOM), an unsupervised learning algorithm, which is simple and self organization, and the common usage of K-means clustering method, this paper combines SOM with K-means to do classification of mov...

We propose a new method called C-SOM using a Self-Organizing Map (SOM) for function approximation. C-SOM takes care about the output values of the «win-ning» neuron's neighbors of the map to compute the output value associated with the input data. Our work extends the standard SOM with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve its gen...

In this article, the use of the self-organizing map (SOM) is approached on the basis of current theories of learning. Possibilities of computer and networked platforms that aim at helping human learning are also inspected. It is shown how the SOM can be considered a model of constructive learning. The area of constructive learning is outlined and two cases of using the self-organizing map in co...

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of cur...