1,721,002 research outputs found
Syllables and Other String Kernel extensions
No full textRecently, the use of string kernels that compare documents as a string of letters has been shown to achieve good results on text classification problems. In this paper we introduce the application of the string kernel in conjunction with syllables. Using syllables shortens the representation of documents and as a result reduces computation time. Moreover syllables provide a more natural representation of text; rather than the traditional coarse representation given by the bag-of-words, or the too fine one resulting from considering individual letters only. We give some experimental results which show that syllables can be effectively used in text-categorisation problems. In this paper we also propose two extensions to the string kernel. The first introduces a new lambda-weighting scheme, where different symbols can be given differing decay weightings. This may be useful in text and other applications where the insertion of certain symbols may be known to be less significant. We also introduce the concept of 'soft matching', where symbols can match (possibly weighted by relevance) even if they are not identical. Again, this provides a method of incorporating prior knowledge where certain symbols can be regarded as a partial or exact match and contribute to the overall similarity measure for two data items
Introduction: Special issue on neural networks and kernel methods for structured domains
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Basic metric learning
No full textThis report presents a a novel Multiple Kernel Learning (MKL) algorithm for the 1-class support vector machine. The emphasis is placed on viewing the CBIR task with relevance feedback as a metric learning problem, where each image has 11 different feature extraction methods applied to it. Our method attempts at finding the most compact ball amongst the 11 different feature representations using a novel 1- and 2-norm regularisation technique for the 1-class SVM under the MKL framework. We also devise a simple way of including the set of negative examples whilst still utilising the 1-class SVM implementation
Using String Kernels to Identify Famous Performers from their Playing Style
No full textIn this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same piece are obtained from changes in beat-level tempo and beat-level loudness, which over the time of the piece form a performance worm. From such worms, general performance alphabets can be derived, and pianists’ performances can then be represented as strings. We show that when using the string kernel on this data, both kernel partial least squares and Support Vector Machines outperform the current best results. Furthermore we suggest a new method of obtaining feature directions from the Kernel Partial Least Squares algorithm and show that this can deliver better performance than methods previously used in the literature when used in conjunction with a Support Vector Machine
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