"Identifying the sentiment of collected tweets has become a challenging and interesting task. In addition, mining defining relevant features that can improve quality classification system is crucial. The data modeling phase fundamental for whole process since it reveal hidden information from textual inputs. Two models are defined in presented paper, considering Twitter-specific concepts: hasht...

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 ...

In this study we investigate using an unsupervised generative learning method for subjectivity detection in text across different domains. We create an initial training set using simple lexicon information, and then evaluate a calibrated EM (expectation-maximization) method to learn from unannotated data. We evaluate this unsupervised learning approach on three different domains: movie data, ne...

Unsupervised learning (clustering) deals with instances, which have not been pre-classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithms is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity....

Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required [1]. Often, network devices are not able to provide information about the type of failure. In such cases, unsupervised s...

One objective for classifying textures in natural images is to achieve the best performance possible. Unsupervised techniques are suitable when no prior knowledge about the image content is available. The main drawback of unsupervised approaches is its worst performance as compared against supervised ones. We propose a new unsupervised hybrid approach based on two welltested classifiers: Vector...