Add to ORKG. A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. classification or conceptual clustering, via robust data analysis algorithms (e.g. k-nearest neighbors or k-means). A method for building a distance on first-order logic domains is presented in this paper. The distance is constructed from examples expressed as definite or constrained clauses, via a two-step process: a set of d hypotheses is first learnt from the training examples. These hypotheses serve as new descriptors of the problem domain Lh : they induce a mapping ß from Lh onto the space of integers IN d . The distance between any two examples E and F is finally defined as the Euclidean distance between ß(E) and ß(F ). The granul...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
In real tasks, usually a good classification performance can only be obtained when a good distance m...
Graduation date: 1995Distance-based algorithms are machine learning algorithms that classify queries...
A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. c...
This paper tackles the supervised induction of a distance from examples described as Horn clauses or...
Several learning systems, such as systems based on clustering and instance based learning, use a mea...
© Springer-Verlag Berlin Heidelberg 1998. Several learning systems, such as systems based on cluster...
Abstract—We consider learning in a transductive setting using instance-based learning (k-NN) and pre...
Supervised classification involves many heuristics, including the ideas of decision tree, k-nearest ...
Normally the distance function used in classification in the k-Nearest Neighbors algorithm is the eu...
The basic concepts of distance based classification are introduced in terms of clear-cut example sys...
Distance functions are an important component in many learning applications. However, the correct fu...
We show how to learn aMahanalobis distance metric for k-nearest neigh-bor (kNN) classification by se...
In machine learning, distance-based algorithms, and other approaches, use information that is repres...
The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learnin...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
In real tasks, usually a good classification performance can only be obtained when a good distance m...
Graduation date: 1995Distance-based algorithms are machine learning algorithms that classify queries...
A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. c...
This paper tackles the supervised induction of a distance from examples described as Horn clauses or...
Several learning systems, such as systems based on clustering and instance based learning, use a mea...
© Springer-Verlag Berlin Heidelberg 1998. Several learning systems, such as systems based on cluster...
Abstract—We consider learning in a transductive setting using instance-based learning (k-NN) and pre...
Supervised classification involves many heuristics, including the ideas of decision tree, k-nearest ...
Normally the distance function used in classification in the k-Nearest Neighbors algorithm is the eu...
The basic concepts of distance based classification are introduced in terms of clear-cut example sys...
Distance functions are an important component in many learning applications. However, the correct fu...
We show how to learn aMahanalobis distance metric for k-nearest neigh-bor (kNN) classification by se...
In machine learning, distance-based algorithms, and other approaches, use information that is repres...
The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learnin...
This thesis is related to distance metric learning for kNN classification. We use the k nearest neig...
In real tasks, usually a good classification performance can only be obtained when a good distance m...
Graduation date: 1995Distance-based algorithms are machine learning algorithms that classify queries...