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Text Classification Using String Kernels
Journal of Machine Learning Research 2 (2002) 419-444 Submitted 12/00;Published 2/02 Text Classification using String Kernels Huma Lodhi [email protected] Craig Saunders [email protected] John Shawe-Taylor [email protected] Nello Cristianini [email protected] Chris Watkins [email protected] Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK Editor: Bernhard Sch¨olkopf Abstract We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences that are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes howdespite this fact the inner product can be efficiently evaluated by a dynamic programming technique. Experimental comparisons of the performance of the kernel compared with a stan- dard word feature space kernel (Joachims, 1998) show positive results on modestly sized datasets. The case of contiguous subsequences is also considered for comparison with the subsequences kernel with different decay factors. For larger documents and datasets the paper introduces an approximation technique that is shown to deliver good approximations efficiently for large datasets. -
Fast String Kernels Using Inexact Matching for Protein Sequences
JournalofMachineLearningResearch5(2004)1435–1455 Submitted 1/04; Revised 7/04; Published 11/04 Fast String Kernels using Inexact Matching for Protein Sequences Christina Leslie [email protected] Center for Computational Learning Systems Columbia University New York, NY 10115, USA Rui Kuang [email protected] Department of Computer Science Columbia University New York, NY 10027, USA Editor: Kristin Bennett Abstract We describe several families of k-mer based string kernels related to the recently presented mis- match kernel and designed for use with support vector machines (SVMs) for classification of pro- tein sequence data. These new kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences (“k-mers”) from the string alphabet Σ. However, for all kernels we define here, the kernel value K(x,y) can be computed in O(cK( x + y )) time, where the constant cK depends on the parameters of the kernel but is inde- pendent| | of the| | size Σ of the alphabet. Thus the computation of these kernels is linear in the length of the sequences, like| | the mismatch kernel, but we improve upon the parameter-dependent con- m+1 m stant cK = k Σ of the (k,m)-mismatch kernel. We compute the kernels efficiently using a trie data structure and| | relate our new kernels to the recently described transducer formalism. In protein classification experiments on two benchmark SCOP data sets, we show that our new faster kernels achieve SVM classification performance comparable to the mismatch kernel and the Fisher kernel derived from profile hidden Markov models, and we investigate the dependence of the kernels on parameter choice. -
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Efficient Global String Kernel with Random Features: Beyond Counting Substructures Lingfei Wu∗ Ian En-Hsu Yen Siyu Huo IBM Research Carnegie Mellon University IBM Research [email protected] [email protected] [email protected] Liang Zhao Kun Xu Liang Ma George Mason University IBM Research IBM Research [email protected] [email protected] [email protected] Shouling Ji† Charu Aggarwal Zhejiang University IBM Research [email protected] [email protected] ABSTRACT of longer lengths. In addition, we empirically show that RSE scales Analysis of large-scale sequential data has been one of the most linearly with the increase of the number and the length of string. crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i) rely on local features of CCS CONCEPTS short substructures in the string, which hardly capture long dis- • Computing methodologies → Kernel methods. criminative patterns, (ii) sum over too many substructures, such as all possible subsequences, which leads to diagonal dominance KEYWORDS of the kernel matrix, or (iii) rely on non-positive-definite similar- String Kernel, String Embedding, Random Features ity measures derived from the edit distance. Furthermore, while there have been works addressing the computational challenge with ACM Reference Format: respect to the length of string, most of them still experience qua- Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, and Charu Aggarwal. 2019. Efficient Global String Kernel dratic complexity in terms of the number of training samples when with Random Features: Beyond Counting Substructures . -
String & Text Mining in Bioinformatics
Data Mining in Bioinformatics Day 9: String & Text Mining in Bioinformatics Karsten Borgwardt February 21 to March 4, 2011 Machine Learning & Computational Biology Research Group MPIs Tübingen Karsten Borgwardt: Data Mining in Bioinformatics, Page 1 Why compare sequences? Protein sequences Proteins are chains of amino acids. 20 different types of amino acids can be found in protein sequences. Protein sequence changes over time by mutations, dele- tion, insertions. Different protein sequences may diverge from one com- mon ancestor. Their sequences may differ slightly, yet their function is often conserved. Karsten Borgwardt: Data Mining in Bioinformatics, Page 2 Why compare sequences? Biological Question: Biologists are interested in the reverse direction: Given two protein sequences, is it likely that they origi- nate from the same common ancestor? Computational Challenge: How to measure similarity between two protein se- quence, or equivalently: How to measure similarity between two strings Kernel Challenge: How to measure similarity between two strings via a ker- nel function In short: How to define a string kernel Karsten Borgwardt: Data Mining in Bioinformatics, Page 3 History of sequence comparison First phase Smith-Waterman BLAST Second phase Profiles Hidden Markov Models Third phase PSI-Blast SAM-T98 Fourth phase Kernels Karsten Borgwardt: Data Mining in Bioinformatics, Page 4 Sequence comparison: Phase 1 Idea Measure pairwise similarities between sequences with gaps Methods Smith-Waterman dynamic programming high accuracy slow (O(n2)) -
Proquest Dissertations
On Email Spam Filtering Using Support Vector Machine Ola Amayri A Thesis in The Concordia Institute for Information Systems Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science (Information Systems Security) at Concordia University Montreal, Quebec, Canada January 2009 © Ola Amayri, 2009 Library and Archives Biblioth&que et 1*1 Canada Archives Canada Published Heritage Direction du Branch Patrimoine de l'6dition 395 Wellington Street 395, rue Wellington Ottawa ON K1A0N4 Ottawa ON K1A0N4 Canada Canada Your file Votre reference ISBN: 978-0-494-63317-5 Our file Notre r6f6rence ISBN: 978-0-494-63317-5 NOTICE: AVIS: The author has granted a non- L'auteur a accorde une licence non exclusive exclusive license allowing Library and permettant a la Bibliothgque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par telecommunication ou par Nntemet, preter, telecommunication or on the Internet, distribuer et vendre des theses partout dans le loan, distribute and sell theses monde, a des fins commerciales ou autres, sur worldwide, for commercial or non- support microforme, papier, electronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette these. Ni thesis. Neither the thesis nor la these ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent etre imprimes ou autrement printed or otherwise reproduced reproduits sans son autorisation. -
Text Classification Using String Kernels
Journal of Machine Learning Research 2 (2002) 419-444 Submitted 12/00;Published 2/02 Text Classification using String Kernels Huma Lodhi [email protected] Craig Saunders [email protected] John Shawe-Taylor [email protected] Nello Cristianini [email protected] Chris Watkins [email protected] Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK Editor: Bernhard Sch¨olkopf Abstract We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences that are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes howdespite this fact the inner product can be efficiently evaluated by a dynamic programming technique. Experimental comparisons of the performance of the kernel compared with a stan- dard word feature space kernel (Joachims, 1998) show positive results on modestly sized datasets. The case of contiguous subsequences is also considered for comparison with the subsequences kernel with different decay factors. For larger documents and datasets the paper introduces an approximation technique that is shown to deliver good approximations efficiently for large datasets.