This paper presents a semi-supervised learning algorithm for a control chart pattern recognition system. A learning neural network is trained with labeled control chart patterns based on unsupervised learning. We then use the classification method based on a statistical correlation coefficient approach to test patterns. We find that the proposed semi-supervised learning algorithm is effective a...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes ...

Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past f...

The landscape of the Niger Delta region of Nigeria is undergoing rapid changes as a result of natural and anthropogenic activities. This necessitates the development of a rapid, cost effective and efficient land cover (LC) classification technique to monitor the biophysical dynamics in the region. Due to the intricate land cover patterns prominent in the study area and the irregularly indisting...

–Clustering techniques make it possible to search large amounts of data for characteristic rules and patterns. To monitoring each data recorded on a cluster or any data mining classification, they can be used to calculate the distance similarity. In this paper, we present “Supervised & Unsupervised learning” a method similarity measures which are used for analysis. The clustering method first p...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...