© Springer-Verlag Berlin Heidelberg 1998. Several learning systems, such as systems based on clustering and instance based learning, use a measure of distance between objects. Good measures of distance exist when objects are described by a fixed set of attributes as in attribute value learners. More recent learning systems however, use a first order logic representation. These systems represent objects as models or clauses. This paper develops a general framework for distances between such objects and reports a preliminary evaluation.status: publishe
Abstract-Relational models are frequently used in high-level com-puter vision. Finding a corresponde...
Abstract. In this paper, we propose to solve multiple instance learning problems using a dissimilari...
The article presents developed in constructive theory of systems, system-wide evaluation formula dis...
Several learning systems, such as systems based on clustering and instance based learning, use a mea...
pp. 281-282 in Proc. NAIC'98, eds. H. La Poutré, J. van den Herik, 1998status: publishe
. A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g....
A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. c...
Abstract. In this work, we introduce a new distance function for data representations based on first...
Instance based learning and clustering are popular methods in propositional machine learning. Both m...
In machine learning, distance-based algorithms, and other approaches, use information that is repres...
Distance-based and generalization-based methods are two families of artificial intelligence techniqu...
In the literature on judgment aggregation, an important open question is how to measure the distance...
Abstract—We consider learning in a transductive setting using instance-based learning (k-NN) and pre...
In the literature on judgment aggregation, an important open question is how to measure the distance...
Abstract. Distance semantics is a robust way of handling dynamically evolving and possibly contradic...
Abstract-Relational models are frequently used in high-level com-puter vision. Finding a corresponde...
Abstract. In this paper, we propose to solve multiple instance learning problems using a dissimilari...
The article presents developed in constructive theory of systems, system-wide evaluation formula dis...
Several learning systems, such as systems based on clustering and instance based learning, use a mea...
pp. 281-282 in Proc. NAIC'98, eds. H. La Poutré, J. van den Herik, 1998status: publishe
. A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g....
A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. c...
Abstract. In this work, we introduce a new distance function for data representations based on first...
Instance based learning and clustering are popular methods in propositional machine learning. Both m...
In machine learning, distance-based algorithms, and other approaches, use information that is repres...
Distance-based and generalization-based methods are two families of artificial intelligence techniqu...
In the literature on judgment aggregation, an important open question is how to measure the distance...
Abstract—We consider learning in a transductive setting using instance-based learning (k-NN) and pre...
In the literature on judgment aggregation, an important open question is how to measure the distance...
Abstract. Distance semantics is a robust way of handling dynamically evolving and possibly contradic...
Abstract-Relational models are frequently used in high-level com-puter vision. Finding a corresponde...
Abstract. In this paper, we propose to solve multiple instance learning problems using a dissimilari...
The article presents developed in constructive theory of systems, system-wide evaluation formula dis...