Add to ORKGWe propose and discuss distance measures to compare objects that have heterogeneous sets of characteristics, such as encountered in, for example, medical diagnosis and information retrieval. We treat both boolean-valued and (scaled) real-valued characteristics. Weighting of characteristics is accomodated. The paper is a modified and extended version of [1]
Distances between data sets are used for analyses such as classification and clustering analyses. So...
Metric learning learns a distance metric from data and has significantly improved the classification...
An interesting and little explored way to understand data is based on prototype rules (P-rules). The...
We propose a distance measure to compare objects that have heterogeneous sets of characteristics, su...
The similarity of objects is one of the most fundamental concepts in any collection of complex infor...
International audienceSimilarity between objects plays an important role in both human cognitive pro...
AbstractRecent years have explored various clustering strategies to partition datasets comprising of...
Measuring the distance between equivalence classes has its theoretical and practical merit, in parti...
Much like in other modeling disciplines does the distance metric used (a measure for dissimilarity) ...
Distance metrics are broadly used in different research areas and applications, such as bio-informat...
Many machine learning methods, such as the k-nearest neighbours algorithm, heavily depend on the dis...
A typical machine learning algorithm takes advantage of training data to discover patterns among obs...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages...
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in ma...
Distances between data sets are used for analyses such as classification and clustering analyses. So...
Metric learning learns a distance metric from data and has significantly improved the classification...
An interesting and little explored way to understand data is based on prototype rules (P-rules). The...
We propose a distance measure to compare objects that have heterogeneous sets of characteristics, su...
The similarity of objects is one of the most fundamental concepts in any collection of complex infor...
International audienceSimilarity between objects plays an important role in both human cognitive pro...
AbstractRecent years have explored various clustering strategies to partition datasets comprising of...
Measuring the distance between equivalence classes has its theoretical and practical merit, in parti...
Much like in other modeling disciplines does the distance metric used (a measure for dissimilarity) ...
Distance metrics are broadly used in different research areas and applications, such as bio-informat...
Many machine learning methods, such as the k-nearest neighbours algorithm, heavily depend on the dis...
A typical machine learning algorithm takes advantage of training data to discover patterns among obs...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages...
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in ma...
Distances between data sets are used for analyses such as classification and clustering analyses. So...
Metric learning learns a distance metric from data and has significantly improved the classification...
An interesting and little explored way to understand data is based on prototype rules (P-rules). The...