Search Results for "knn algorithm"

Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying...

• Objective: Design a Web-based software that predicts the appearance of a new link between two nodes in a social network • Datasets: Dolphin Social Network: https://networkdata.ics.uci.edu/data.php?id=6 • Requirement: Implement the K-NN Algorithm (Section 6.9.1, page 348)

This implements KNN algorithm.

Speed up kNN searching algorithm by extracting nearest neighbors diectly other than searching them one by one

A fast artificial intelligence library which currently supports: kNN (k-Nearest Neighbor algorithm) MLP (Multilayer-Perceptron)

gaKnn(Genetic Algorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a genetic algorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).

Basic implementation of K-nearest neighbour Algorithm and the application of KNN to classify protein sequences as transmembrane beta barrel or non-transmembrane beta barrel on the basis of whole sequence amino acid composition given as input.

KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.

A Visual Studio .NET C++ application can perform machine learning using genetic algorithm, naive bayes, KNN, and Artificial Neural Networks (ANNs) read and processed from any standard ARFF.