In this project we present a heuristic learning process by training ANN (artificial neural network) and KNN (k-nearest neighbor) using the best number of steps, gained from A*, from randomly generated states to the goal. After training ANN and KNN, the mixture of Experts is discussed and the empirical data are collected to demonstrate the feasibility and accuracy of combination of ANN and KNN i...

Interpretability, usability, and boundary points detection of the clustering results are of fundamental importance. The cluster boundary detection is important for many real world applications, such as geo-spatial data analysis and point-based computer graphics. In this paper, we have proposed an efficient solution for finding boundary points in multi-dimensional datasets that uses the BORDER [...

Four versions of a k-nearest neighbor algorithm with locally adaptive k are introduced and compared to the basic k-nearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross-validation computations in the local neighborhood of the query. Local kNN methods are shown to perform similar to kNN i...

K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both applet and MATLAB program with real life benchmark data to analyze the performan...

The Optimally Pruned Extreme Learning Machine (OPELM) and Optimally Pruned K-Nearest Neighbors (OP-KNN) algorithms use the a similar methodology based on random initialization (OP-ELM) or KNN initialization (OP-KNN) of a Feedforward Neural Network followed by ranking of the neurons; ranking is used to determine the best combination to retain. This is achieved by Leave-One-Out (LOO) crossvalidat...

This paper analyzes the performance of different classification methods for online activity recognition on smart phones using the built-in accelerometers. First, we evaluate the performance of activity recognition using the Naïve Bayes classifier and next we utilize an improvement of Minimum Distance and K-Nearest Neighbor (KNN) classification algorithms, called Clustered KNN. For the purpose o...