INTRODUCTION K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. CASE DESCRIPTION Since the Euclidean distance function is the most widely us...

This paper presents the results of the application of an instance-based learning algorithm k-Nearest Neighbor Method on Feature Projections (k-NNFP) to text categorization and compares it with k-Nearest Neighbor Classiier (k-NN). k-NNFP is similar to k-NN except it nds the nearest neighbors according to each feature separately. Then it combines these predictions using a majority voting. This pr...

The energy spectra of light-mass kaonic nuclei were investigated using the theoretical framework $0s$-orbital model with zero-range $\bar{K} N$ and $\bar{K}\bar{K}$ interactions effective single-channel real potentials. energies NN$, NNN$, NNNN$, $\bar{K}\bar{K} N$, NN$ systems calculated in cases weak- deep-binding interaction, which was adjusted to fit $\Lambda(1405)$ mass bound state. result...

This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples a...

In this paper we describe work relating to classification of web documents using a graph-based model instead of the traditional vector-based model for document representation. We compare the classification accuracy of the vector model approach using the kNearest Neighbor (k-NN) algorithm to a novel approach which allows the use of graphs for document representation in the k-NN algorithm. The pr...

This work presents a study about feature selection and weighting for improving the recognition of handwritten words coming from Brazilian bank check lexicon. For this purpose, two global optimization methods are used: Tabu Search(TS) and Simulated Annealing(SA). These methods were combined with k-NN composing two hybrid approaches for features selection and weighting: SA/k-NN and TS/k-NN. The r...

In this paper we evaluate an instance-based spam filter based on the SVM nearest neighbor (SVM-NN) classifier, which combines the ideas of SVM and k-nearest neighbor. To label a message the classifier first finds k nearest labeled messages, and then an SVM model is trained on these k samples and used to label the unknown sample. Here we present preliminary results of the comparison of SVM-NN wi...

The k–Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified ...