Introduction Classification is a fundamental image processing operation to extract information from remote sensing data. Both crisp and fuzzy classifications may be performed. In a crisp classification, each image pixel is assumed to be pure and is classified to one class. Often, particularly in coarse spatial resolution images, the pixels may be mixed containing two or more classes. Fuzzy clas...

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 ...

This paper gives a brief description of a hierarchical architecture (HONG) that has been described elsewhere. The learning algorithm it uses is a mixed unsupervised/supervised method with most of the learning being unsupervised. The architecture generates multiple classifications for every data pattern presented, and combines them to obtain the final classification. The main purpose of this pap...

Pattern recognition on seismic data is a useful technique for generating seismic facies maps that capture changes in the geological depositional setting. Seismic facies analysis can be performed using the supervised and unsupervised pattern recognition methods. Each of these methods has its own advantages and disadvantages. In this paper, we compared and evaluated the capability of two unsuperv...

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 ...

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing supervised or unsupervised classification methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sen...

This paper presents an unsupervised approach for dialogue act (DA) classification. We used a latent variable model to compress the dimensions of the feature vector. We introduced a paraphraser to reduce the variety of expressions and to solve the pragmatic problem for DA classification. The paraphraser seemed to work well on some DA classifications in the unsupervised approach. The results obta...

This paper presents a new hybrid learning algorithm for unsupervised classification tasks. We combined Fuzzy c-means learning and the supervised version of Minimerror to develop a hybrid incremental strategy allowing unsupervised classifications. We applied this new approach to a real-world database in order to know if the information contained in unlabeled signals of a Geographic Information S...

This paper presents the use of satellite data (i.e., Landsat-5 & Landsat-8) to interpret change land cover from 1997 2020. The study area covers administrative boundary Lumajang Regency. land-cover map year derived Landsat-5. Land-cover 2020 interpreted Landsat-8. uses two methods image classifications unsupervised and supervised). procedure includes enhancement, registration, classificatio...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...