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On Graph Kernels: Hardness Results and Efficient Alternatives
TLDR
We propose a generalized family of graph kernels based on walks which includes the kernels proposed in [4] as special cases while still being polynomially computable. Expand
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Multi-Instance Kernels
TLDR
We propose a kernel on multi-instance data that can be shown to separate positive and negative sets under natural assumptions. Expand
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A survey of kernels for structured data
TLDR
Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Expand
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Cyclic pattern kernels for predictive graph mining
TLDR
In this paper, we show that with a natural set of patterns, cyclic and tree patterns, it is possible to eliminate the restriction to frequent patterns. Expand
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Direct local pattern sampling by efficient two-step random procedures
TLDR
We present several exact and highly scalable local pattern sampling algorithms. Expand
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Expressivity versus efficiency of graph kernels
TLDR
We study the trade-off between expressivity and efficiency of graph kernels and propose a new graph kernel based on subtree patterns. Expand
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Efficient co-regularised least squares regression
TLDR
We investigate a semi-supervised least squares regression algorithm based on the co-learning approach that scales linearly in the number of unlabelled examples. Expand
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Label Ranking Algorithms: A Survey
TLDR
Label ranking is a complex prediction task where the goal is to map instances to a total order over a finite set of predefined labels, but also to rank them according to the nature of the input. Expand
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Player Modeling for Intelligent Difficulty Adjustment
TLDR
In this paper we aim at automatically adjusting the difficulty of computer games by clustering players into different types and supervised prediction of the type. Expand
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Kernels and Distances for Structured Data
TLDR
We propose a general method for constructing a kernel following the syntactic structure of the data, as defined by its type signature in a higher order logic. Expand
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