Effects of distance measure choice on k-nearest neighbor classifier performance: a review
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …
yet its performance competes with the most complex classifiers in the literature. The core of …
Distance and similarity measures effect on the performance of K-nearest neighbor classifier--a review
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …
yet its performance competes with the most complex classifiers in the literature. The core of …
[BOOK][B] Introduction to machine learning
E Alpaydin - 2020 - books.google.com
A substantially revised fourth edition of a comprehensive textbook, including new coverage
of recent advances in deep learning and neural networks. The goal of machine learning is to …
of recent advances in deep learning and neural networks. The goal of machine learning is to …
KNN model-based approach in classification
Abstract The k-Nearest-Neighbours (kNN) is a simple but effective method for classification.
The major drawbacks with respect to kNN are (1) its low efficiency–being a lazy learning …
The major drawbacks with respect to kNN are (1) its low efficiency–being a lazy learning …
Prototype selection for nearest neighbor classification: Taxonomy and empirical study
The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …
performing recognition tasks. It has also demonstrated itself to be one of the most useful …
Survey of nearest neighbor techniques
N Bhatia - arxiv preprint arxiv:1007.0085, 2010 - arxiv.org
The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field
of pattern recognition, text categorization, object recognition etc. Its simplicity is its main …
of pattern recognition, text categorization, object recognition etc. Its simplicity is its main …
[BOOK][B] Fuzzy classifier design
L Kuncheva - 2000 - books.google.com
Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever
since have been a center of many discussions, fervently admired and condemned. Both …
since have been a center of many discussions, fervently admired and condemned. Both …
Solving the problem of the K parameter in the KNN classifier using an ensemble learning approach
This paper presents a new solution for choosing the K parameter in the k-nearest neighbor
(KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak …
(KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak …
Wheat seed classification: utilizing ensemble machine learning approach
Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain
supply chain, particularly for methods for seed inspection. Recognition and verification of …
supply chain, particularly for methods for seed inspection. Recognition and verification of …
[PDF][PDF] Applying k-nearest neighbour in diagnosing heart disease patients
M Shouman, T Turner, R Stocker - International Journal of …, 2012 - academia.edu
Heart disease is the leading cause of death in the world over the past 10 years. Researchers
have been using several data mining techniques to help health care professionals in the …
have been using several data mining techniques to help health care professionals in the …