Today, most of the data in business applications is stored in relational database systems or in data warehouses built on top of relational database systems. Often, for more data is available than can be processed by standard learning algorithms in reasonable time. This paper presents an extension to kernel algorithms that makes use of the more compact relational representation of data instead of the usual attribute-value representation to significantly speed up the kernel calculation
Due to the growing amount of digital data stored in relational databases, more new approaches are re...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
We develop a general theoretical framework for statistical logical learning with kernels based on dy...
Multirelational classification aims to discover patterns across multiple interlinked tables (relatio...
Abstract. In this paper we present a novel and general framework for kernel-based learning over rela...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
This paper gives a k-means approximation algorithm that is efficient in the relational algorithms mo...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
The primary difference between propositional (attribute-value) and relational data is the existence ...
The goal of this dissertation is to examine various aspects of the distance- and kernel-based learni...
Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relatio...
Kernel methods are popular and effective techniques for learn- ing on structured data, such as trees...
Multi-relational data mining algorithms search a large hypothesis space in order to find a suitable ...
Due to the growing amount of digital data stored in relational databases, more new approaches are re...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
We develop a general theoretical framework for statistical logical learning with kernels based on dy...
Multirelational classification aims to discover patterns across multiple interlinked tables (relatio...
Abstract. In this paper we present a novel and general framework for kernel-based learning over rela...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
This paper gives a k-means approximation algorithm that is efficient in the relational algorithms mo...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
The primary difference between propositional (attribute-value) and relational data is the existence ...
The goal of this dissertation is to examine various aspects of the distance- and kernel-based learni...
Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relatio...
Kernel methods are popular and effective techniques for learn- ing on structured data, such as trees...
Multi-relational data mining algorithms search a large hypothesis space in order to find a suitable ...
Due to the growing amount of digital data stored in relational databases, more new approaches are re...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
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