The Internet has been in a state of explosive expansion over the last decade and a half. The addition of numerous web pages to the World Wide Web by a vast array of authors on a plethora of topics leaves behind the problem of organizing these web pages in order to improve search results leading to more relevant information. In this paper, a modified attribute weighted dynamic k-Nearest Neighbor...

Nearest neighbor (NN) classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The NN rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local ...

Abstract We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The is based on nearest-neighbor architecture. It modern instantiation the vanilla k-nearest neighbor model, as we use Transformer representations all its components. Our can adapt to new source- instances, w...

This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed consists of three mechanisms. First, we set up (MNN) and train it with historical information. Some the initial solutions are generated by MNN when an environmental change is detected. Second, some predicted forward-looking center p...

First-stage feature computation and data rate reduction play a crucial role in an efficient visual information processing system. Hardware-based first stages usually win out where power consumption, dynamic range, and speed are the issue, but have severe limitations with regard to flexibility. In this paper, the local orientation coding (LOC), a nearest neighborhood grayscale operator, is inves...

A novel algorithm called independent subspace analysis (ISA) is introduced to estimate independent subspaces. The algorithm solves the ISA problem by estimating multi-dimensional di erential entropies. Two variants are examined, both of them utilize distances between the k-nearest neighbors of the sample points. Numerical simulations demonstrate the usefulness of the algorithms.

This paper presents a simple and computationally good method for plant species recognition using leaf images. Recognition of plant images is one of the research topics of computer vision. The use of shape for recognizing objects has been actively studied since the beginning of object recognition in 1950s. Several authors suggest that object shape is more informative than its appearance properti...

Conceptual blending is an important area of research for creativity modeling. In this paper we present a creativity model that takes an existing blend and generates new blends using the nearest neighborhood replacements from a lexical ontology. For example “Artificial Intelligence” is a compounded concept comprising of “Artificiality” and “Intelligence” as two sub-concepts. After concept genera...

Collaborative Filtering (CF) is the most popular choice when implementing personalized recommender systems. A classical approach to CF is based on K-nearest-neighborhood (KNN) model, where the precondition for making recommendations is the KNN construction for involved entities. However, when building KNN sets, there exits the dilemma to decide the value of K --a small value will lead to poor r...