k nearest neighbor (kNN) is an effective and powerful lazy learning algorithm, notwithstanding its easy-to-implement. However, its performance heavily relies on the quality of training data. Due to many complex real-applications, noises coming from vari
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
We present Stochastic Neighbor Compression (SNC), an algorithm to compress a dataset for the purpose...
Abstract. This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm ...
2008 4th International IEEE Conference Intelligent Systems, IS 2008 --6 September 2008 through 8 Sep...
The k Nearest Neighbors (kNN) method is a widely used technique to solve classification or regressio...
The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and mach...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classificati...
Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common ...
In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
K-Nearest Neighbor (K-NN) is a classification technique that makes explicit predictions on test data...
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
We present Stochastic Neighbor Compression (SNC), an algorithm to compress a dataset for the purpose...
Abstract. This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm ...
2008 4th International IEEE Conference Intelligent Systems, IS 2008 --6 September 2008 through 8 Sep...
The k Nearest Neighbors (kNN) method is a widely used technique to solve classification or regressio...
The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and mach...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classificati...
Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common ...
In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
K-Nearest Neighbor (K-NN) is a classification technique that makes explicit predictions on test data...
Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
We present Stochastic Neighbor Compression (SNC), an algorithm to compress a dataset for the purpose...