Improved KNN with Feedback Support

Improved Evidence Theoretic kNN Classifier based on Theory of Evidence

Improved Target Acquisition Rates with Feedback Codes

This paper considers the problem of acquiring an unknown target location (among a finite number of locations) via a sequence of measurements, where each measurement consists of simultaneously probing a group of locations. The resulting observation consists of a sum of an indicator of the target’s presence in the probed region, and a zero mean Gaussian noise term whose variance is a function of ...

Associative Classification with Knn

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...

An Improved KNN Text Classification Algorithm Based on Clustering

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 ...

Speeding up active relevance feedback with approximate kNN retrieval for hyperplane queries

In content-based image retrieval, relevance feedback is a prominent method for reducing the “semantic gap” between the low-level features describing the content and the usually higher-level meaning of user’s target. Recent relevance feedback methods are able to identify complex target classes after relatively few feedback iterations. However, since the computational complexity of such methods i...