A reinforcement learning algorithm called kNN-TD is introduced. This algorithm has been developed using the classical formulation of temporal difference methods and a k-nearest neighbors scheme as its expectations memory. By means of this kind of memory the algorithm is able to generalize properly over continuous state spaces and also take benefits from collective action selection and learning ...

With the availability of huge amount of text in internet, news, institutes, organization etc need of automatic text classification also increases, The proposed work comprised to deal with the major challenge of getting labeled data for training in classifier, since the availability of labeled data is expensive, time consuming, it also requires the involvement of annotator . A novel semi supervi...

ABSTRACTIn today‟s library science, information and computer science, online text classification or text categorization is a huge complication. [1]With the enormous growth of online information and data, text categorization has become one of the crucial techniques for handling and standardizing text data. Various learning algorithms have been applied on text for categorization. On the basis of ...

The traditional KNN text classification algorithm used all training samples for classification, so it had a huge number of training samples and a high degree of calculation complexity, and it also didn’t reflect the different importance of different samples. In allusion to the problems mentioned above, an improved KNN text classification algorithm based on clustering center is proposed in this ...

Associative classification usually generates a large set of rules. Therefore, it is inevitable that an instance matches several rules which classes are conflicted. In this paper, a new framework called Associative Classification with KNN (AC-KNN) is proposed, which uses an improved KNN algorithm to address rule conflicts. Traditional K-Nearest Neighbor (KNN) is low efficient due to its calculat...

electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...