K-nearest neighbors (KNN) is a simple and intuitive machine learning algorithm that can be used for classification and regression tasks. It works by finding the k most similar instances in the ...

In this study, colors are classified by using K-Neares Neşghbor Machine Learning classifier algorithm. This classifier is trained by image R, G, B Color Histogram values. The general work flow is ...

Cosine similarity in machine learning can be used for classification tasks wherein it can be used as a metric in the KNN classification algorithms to find the optimal number of neighbors and also the ...

K-nearest neighbor. The theory of the kNN algorithm (Peterson, 2009) is relatively mature, and it is also a commonly used supervised learning algorithm. It does not try to build a general internal ...

SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set ...

This paper compare and analyse the performance of three machine learning algorithm to do the task of classifying human facial expression. The total of 23 variables calculated from the distance of ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. ... aka KNN (for both classification and regression) ...

Since Random Forest is a low-level algorithm in machine learning architectures, it can also contribute to the performance of other low-level methods, as well as visualization algorithms, including ...

Machine learning classifies data for enhanced algorithm performance in various applications. Lazy learners and eager learners represent key categories of classification algorithms. Effective data ...