The definition of appropriate kernel functions is crucial for the performance of a kernel method. In many of the state-of-the-art kernels for trees, matching substructures are considered independently from their position within the trees. However, when a match happens in similar positions, more strength could reasonably be given to it. Here, we give a systematic way to enrich a large class of tree kernels with this kind of information without affecting, in almost all cases, the worst case computational complexity. Experimental results show the effectiveness of the proposed approach
This paper brings together two strands of machine learning of increasing importance: kernel methods ...
In this paper we present a new algorithm suitable for matching discrete objects such as strings and ...
In this paper, we propose the distributed tree ker-nels (DTK) as a novel method to reduce time and s...
Tree kernels proposed in the literature rarely use information about the relative location of the su...
Almost all tree kernels proposed in the literature match substructures without taking into account t...
Graph kernels are usually defined in terms of simpler kernels over local substructures of the origin...
Abstract — Kernel methods are effective approaches to the modeling of structured objects in learning...
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms...
Machine learning comprises a series of techniques for automatic extraction of meaningful information...
This paper presents a family of methods for the design of adaptive kernels for tree-structured data ...
<p>For trees comprising and nodes, respectively, there are pairs of nodes to evaluate. (A) Starti...
In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and sp...
Tree Kernel functions are powerful tools for solving different classes of problems requiring large a...
We present a method for speeding up the calculation of tree kernels during training. The calculation...
Kernel-based learning methods are primarily used with real-valued data. Yet many domains are made up...
This paper brings together two strands of machine learning of increasing importance: kernel methods ...
In this paper we present a new algorithm suitable for matching discrete objects such as strings and ...
In this paper, we propose the distributed tree ker-nels (DTK) as a novel method to reduce time and s...
Tree kernels proposed in the literature rarely use information about the relative location of the su...
Almost all tree kernels proposed in the literature match substructures without taking into account t...
Graph kernels are usually defined in terms of simpler kernels over local substructures of the origin...
Abstract — Kernel methods are effective approaches to the modeling of structured objects in learning...
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms...
Machine learning comprises a series of techniques for automatic extraction of meaningful information...
This paper presents a family of methods for the design of adaptive kernels for tree-structured data ...
<p>For trees comprising and nodes, respectively, there are pairs of nodes to evaluate. (A) Starti...
In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and sp...
Tree Kernel functions are powerful tools for solving different classes of problems requiring large a...
We present a method for speeding up the calculation of tree kernels during training. The calculation...
Kernel-based learning methods are primarily used with real-valued data. Yet many domains are made up...
This paper brings together two strands of machine learning of increasing importance: kernel methods ...
In this paper we present a new algorithm suitable for matching discrete objects such as strings and ...
In this paper, we propose the distributed tree ker-nels (DTK) as a novel method to reduce time and s...