Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. ...

The use of “nearest-neighbor” sampling has a long history. It involves measuring the distance from a random point in an area to the nearest object. That history involves never quite solving the problem, many examinations of special cases that never occur, adjustments that were ad-hoc, and a great deal of uninformative algebra. In forestry we have attempted to use the “nearest-tree” method for e...

We explore a variety of nearest neighbor baseline approaches for image captioning. These approaches find a set of nearest neighbor images in the training set from which a caption may be borrowed for the query image. We select a caption for the query image by finding the caption that best represents the “consensus” of the set of candidate captions gathered from the nearest neighbor images. When ...

We discuss the role of nearest neighbor The nearest neighbor method can essential be seen technologies in case based reasoning. We describe the to involve a two step process. The first step in the use of the ordered weighted averaging for the nearest neighbor approach is to calculate the similarity development of nearest neighbor rules. A procedure for of the object of interest to each of the o...

Nearest neighbor classifier is a widely-used effective method for multi-class problems. However, it suffers from the problem of the curse of dimensionality in high dimensional space. To solve this problem, many adaptive nearest neighbor classifiers were proposed. In this paper, a locally adaptive nearest neighbor classification method based on supervised learning style which works well for the ...

The first step in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper advocates using the less well-known mutual k-nearest neighbor graphs for high-dimensional natural language data. To compare the performance of these two graph construction methods, we ru...

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...

Motivation: With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein β-turn prediction. In this study, we perform protein β-turn prediction using a k -nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure i...