Distance-based classification methods such as the nearest-neighbor and k-nearest-neighbor classifiers have to rely on a metric or distance measure between points in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance measures have to be used. In this paper, we propose a novel kernel-based method that achieves Euc...

Given a set V of n vectors in d-dimensional space, we provide an efficient method for computing quality upper and lower bounds of the Euclidean distances between a pair of vectors in V . For this purpose, we define a distance measure, called the MS-distance, by using the mean and the standard deviation values of vectors in V . Once we compute the mean and the standard deviation values of vector...

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study large-scale social restrictions (PSBB). This aims classify negative positive by applying K-Nearest Neighbor algorithm see accuracy value 3 types distance calculation are cosine similarity, euclidean, manhattan for Indonesian language tweets (PSBB)...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

The text-based classification dominates in the conventional audio classification systems, in which tedious manual work is used to notate the name, class, or sample rate. However, on most occasions, this method is not satisfying due to its opaque to real content. In order to retrieve the audio files effectively and efficiently, content-based audio classification becomes more and more necessary. ...

In this paper, we report progress on answering the open problem presented by Pagh [11], who considered the nearest neighbor search without false negatives for the Hamming distance. We show new data structures for solving the c-approximate nearest neighbors problem without false negatives for Euclidean high dimensional space R. These data structures work for any c = ω( √ log logn), where n is th...

Building an index tree is a common approach to speed up the k nearest neighbour search in large databases of many-dimensional records. Many applications require varying distance metrics by putting a weight on diierent dimensions. The main problem with k nearest neighbour searches using weighted euclidean metrics in a high dimensional space is whether the searches can be done eeciently We presen...

This paper proposes a novel k-nearest neighbor algorithm to predict soil moisture in maize field. In order to estimate soil moisture in maize field accurately without any destruction to root and soil, this paper uses biological characteristics of maize to estimate soil moisture, including plant height, leaf area, stem diameter, dry weight and fresh weight, all the values of which are non-negati...

This paper describes a comparison of approaches for time series classification. Our comparisons included two different outlier removal methods (discords and reverse nearest neighbor), two different distance measures (Euclidean distance and dynamic time warping), and two different classification algorithms (k nearest neighbor and support vector machines). An algorithm for semi-supervised learnin...