This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an impro...

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection S...

This paper proposed a new approach to design the system using a hybrid of misuse and anomaly detection for training of normal and attack packets respectively. The utilized method for attack training is the combination of unsupervised and supervised Neural Network (NN) for Intrusion Detection System. By the unsupervised NN based on Self Organizing Map (SOM), attacks will be classified into small...

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that micro...

In this paper, we present a novel hybrid keyword spotting system that combines supervised and semi-supervised competitive learning algorithms. The rst stage is a S-SOM (Semi-supervised SelfOrganizing Map) module which is speci cally designed for discrimination between keywords (KWs) and non-keywords (NKWs). The second stage is an FDVQ (Fuzzy Dynamic Vector Quantization) module which consists of...

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-e...

Clustering is a discovering process of meaningful intbrmation by grouping similar data into compact clusters. Most of traditional clustering methods are in favor of small datasets and have difficulties handling very large datasets. They are not adequate clustering methods for partitioning huge datasets in data mining perspective. We propose a new clustering technique, HRC(hierarchical represent...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A theoretical and practical challenge in the SOM has been the diffi­ culty to treat the method as a statistical model fitting procedure. In this chapter we give a short review of statistical approaches for the SOM. Then we present the probability density model for which the SOM training gives the maximum likeli­ h...

Recently, clustering algorithms combined conventional methods and artificial intelligence. FSCSOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster ...

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this i...