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Instance Based Learning (IBL) results in classifying a new instance by examining and comparing it to the rest of the instances in the dataset. An example of this type of learning is the K-Nearest Neighbor algorithm which is based on examining an average Euclidian distance of the nearest k neighbors ' parameters given a certain situation
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nea...
This thesis is specialized in instance based learning algorithms. Main goal is to create an applicat...
ii Instance-based learning is a machine learning method that classifies new examples by comparing th...
The presented thesis focuses on instance-based learning (IBL) methods. The groundwork of instance-ba...
Instance-based learning is a machine learning method that classifies new examples by comparing them ...
This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic a...
AbstractThis paper presents average-case analyses of instance-based learning algorithms. The algorit...
This paper presents a novel instance-based learning methodology the Binomial-Neighbour (B-N) algorit...
The basic nearest-neighbor rule generalizes well in many domains but has several shortcomings, inclu...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
Instance-based learning (IBL) methods predict the class label of a new instance based directly on th...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nea...
This thesis is specialized in instance based learning algorithms. Main goal is to create an applicat...
ii Instance-based learning is a machine learning method that classifies new examples by comparing th...
The presented thesis focuses on instance-based learning (IBL) methods. The groundwork of instance-ba...
Instance-based learning is a machine learning method that classifies new examples by comparing them ...
This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic a...
AbstractThis paper presents average-case analyses of instance-based learning algorithms. The algorit...
This paper presents a novel instance-based learning methodology the Binomial-Neighbour (B-N) algorit...
The basic nearest-neighbor rule generalizes well in many domains but has several shortcomings, inclu...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
Instance-based learning (IBL) methods predict the class label of a new instance based directly on th...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
We consider improving the performance of k-Nearest Neighbor classifiers. A reg-ularized kNN is propo...
Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nea...
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