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1,262 results on '"String kernel"'

1. A Brief Review on Protein Classification Based on Functional, Behavioral, and Structural Properties Using Data Mining Techniques

Author

Majumdar, Stuti, Saha, Suprativ, Bhattacharya, Tanmay, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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2. String Kernel-Based Techniques for Native Language Identification

Author

Vamshi Kumar Gurram, J. Sanil, V. S. Anoop, and S. Asharaf

Subjects
Kernel methods, Native language identification, String kernel, Text feature extraction, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract In recent years, Native Language Identification (NLI) has shown significant interest in computational linguistics. NLI uses an author’s speech or writing in a second language to figure out their native language. This may find applications in forensic linguistics, language teaching, second language acquisition, authorship attribution, identification of spam emails or phishing websites, etc. Conventional pairwise string comparison techniques are computationally expensive and time-consuming. This paper presents fast NLI techniques based on string kernels such as spectrum, presence bits, and intersection string kernels incorporating different learners such as a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting-XGBoost (XGB). Feature sets for the proposed techniques are generated using different combinations of features such as n-word grams and noun phrases. Experimental analyses are carried out using 8235 English as a second language articles from 10 different linguistic backgrounds from a typical NLP benchmark dataset. The experimental results show that the proposed NLI technique incorporating a spectrum string kernel with an RF classifier outperformed existing character n-gram string kernels incorporating SVM, RF, and XGB classifiers. Also, comparable results were observed among different combinations of string kernels. Interestingly, the random forest classifier outperformed SVM and XGB classifiers with different feature sets. All the proposed NLI techniques demonstrated promising results with significant improvement in training time, with the best result attaining more than a 95 percent decrease in training time. The reduced training time of proposed techniques makes it well suited to scale NLI applications for production.

Published
2023
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3. String Kernel-Based Techniques for Native Language Identification.

Author

Gurram, Vamshi Kumar, Sanil, J., Anoop, V. S., and Asharaf, S.

Subjects
NATIVE language, MACHINE learning, DEEP learning, ARTIFICIAL intelligence, WEBSITES
Abstract

In recent years, Native Language Identification (NLI) has shown significant interest in computational linguistics. NLI uses an author's speech or writing in a second language to figure out their native language. This may find applications in forensic linguistics, language teaching, second language acquisition, authorship attribution, identification of spam emails or phishing websites, etc. Conventional pairwise string comparison techniques are computationally expensive and time-consuming. This paper presents fast NLI techniques based on string kernels such as spectrum, presence bits, and intersection string kernels incorporating different learners such as a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting-XGBoost (XGB). Feature sets for the proposed techniques are generated using different combinations of features such as n-word grams and noun phrases. Experimental analyses are carried out using 8235 English as a second language articles from 10 different linguistic backgrounds from a typical NLP benchmark dataset. The experimental results show that the proposed NLI technique incorporating a spectrum string kernel with an RF classifier outperformed existing character n-gram string kernels incorporating SVM, RF, and XGB classifiers. Also, comparable results were observed among different combinations of string kernels. Interestingly, the random forest classifier outperformed SVM and XGB classifiers with different feature sets. All the proposed NLI techniques demonstrated promising results with significant improvement in training time, with the best result attaining more than a 95 percent decrease in training time. The reduced training time of proposed techniques makes it well suited to scale NLI applications for production. [ABSTRACT FROM AUTHOR]

Published
2023
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4. A weighted string kernel for protein fold recognition

Author

Nojoomi, Saghi and Koehl, Patrice

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Algorithms, Amino Acids, Area Under Curve, Principal Component Analysis, Protein Folding, Proteins, ROC Curve, String kernel, Protein fold recognition, Amino acid substitution matrices, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

BackgroundAlignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects.MethodsIn this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences.ResultsUsing a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids.ConclusionWe have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.

Published
2017

5. Protein Sequence Classification Involving Data Mining Technique: A Review

Author

Saha, Suprativ, Bhattacharya, Tanmay, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Elçi, Atilla, editor, Sa, Pankaj Kumar, editor, Modi, Chirag N., editor, Olague, Gustavo, editor, Sahoo, Manmath N., editor, and Bakshi, Sambit, editor

Published
2020
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7. A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine

Author

Ünal, Ali Burak, Akgün, Mete, Pfeifer, Nico, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mu, Yi, editor, Deng, Robert H., editor, and Huang, Xinyi, editor

Published
2019
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8. UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method

Author

Basma Hassan, Samir E. Abdelrahman, Reem Bahgat, and Ibrahim Farag

Subjects
Semantic textual similarity, word alignment, string kernel, BabelNet, SemEval, text processing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in the state-of-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.

Published
2019
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9. Linear-time String Indexing and Analysis in Small Space.

Author

BELAZZOUGUI, DJAMAL, CUNIAL, FABIO, KÄRKKÄINEN, JUHA, and MÄKINEN, VELI

Subjects
TIME management, GENETIC techniques, SPACETIME, DATA structures, NUCLEOTIDE sequence
Abstract

The field of succinct data structures has flourished over the past 16 years. Starting from the compressed suffix array by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-Wheeler transform (BWT) have been developed, all taking an amount of space that is close to the input size in bits. In many largescale applications, the construction of the index and its usage need to be considered as one unit of computation. For example, one can compare two genomes by building a common index for their concatenation and by detecting common substructures by querying the index. Efficient string indexing and analysis in small space lies also at the core of a number of primitives in the data-intensive field of high-throughput DNA sequencing. We report the following advances in string indexing and analysis: We show that the BWT of a string T â {1, . . ., σ }n can be built in deterministic O(n) time using just O(n log σ) bits of space, where σ ⤠n. Deterministic linear time is achieved by exploiting a new partial rank data structure that supports queries in constant time and that might have independent interest. Within the same time and space budget, we can build an index based on the BWT that allows one to enumerate all the internal nodes of the suffix tree of T. Many fundamental string analysis problems, such as maximal repeats, maximal unique matches, and string kernels, can be mapped to such enumeration and can thus be solved in deterministic O(n) time and in O(n log σ) bits of space fromthe input string by tailoring the enumeration algorithm to some problem-specific computations. We also show how to build many of the existing indexes based on the BWT, such as the compressed suffix array, the compressed suffix tree, and the bidirectional BWT index, in randomized O(n) time and in O(n log σ) bits of space. The previously fastest construction algorithms for BWT, compressed suffix array and compressed suffix tree, which used O(n log σ) bits of space, took O(n log log σ) time for the first two structures and O(n logϵ n) time for the third, where ϵ is any positive constant smaller than one. Alternatively, the BWT could be previously built in linear time if onewas willing to spendO(n log σ log logσ n) bits of space. Contrary to the state-of-the-art, our bidirectional BWT index supports every operation in constant time per element in its output. [ABSTRACT FROM AUTHOR]

Published
2020
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11. A Fast Algorithm for Local Rank Distance: Application to Arabic Native Language Identification

Author

Ionescu, Radu Tudor, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arik, Sabri, editor, Huang, Tingwen, editor, Lai, Weng Kin, editor, and Liu, Qingshan, editor

Published
2015
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12. Using Semantic Models for Robust Natural Language Human Robot Interaction

Author

Bastianelli, Emanuele, Croce, Danilo, Basili, Roberto, Nardi, Daniele, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Gavanelli, Marco, editor, Lamma, Evelina, editor, and Riguzzi, Fabrizio, editor

Published
2015
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13. A weighted string kernel for protein fold recognition

Author

Saghi Nojoomi and Patrice Koehl

Subjects
String kernel, Protein fold recognition, Amino acid substitution matrices, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects. Methods In this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences. Results Using a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids. Conclusion We have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.

Published
2017
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14. Hardware Acceleration of the STRIKE String Kernel Algorithm for Estimating Protein to Protein Interactions

Author

Fadi N. Sibai, Ali A. El-Moursy, Sohaib Majzoub, and Abu Asaduzzaman

Subjects
Multi-core processor, Computers, Computer science, Applied Mathematics, Acceleration, Volume (computing), Computational Biology, Proteins, Kernel (linear algebra), ComputingMethodologies_PATTERNRECOGNITION, String kernel, Genetics, Verilog, Hardware acceleration, Field-programmable gate array, Algorithm, computer, Algorithms, Biotechnology, computer.programming_language
Abstract

Protein-protein interaction (PPI) is an important field in bioinformatics which helps in understanding diseases and devising therapy. PPI aims at estimating the similarity of protein sequences and their common regions. STRIKE was introduced as a PPI algorithm which was able to achieve reasonable improvement over existing PPI prediction methods. Although it consumes a lower execution time than most of other state-of the-art PPI prediction methods, its compute-intensive nature and the large volume of protein sequences in protein databases necessitate further time acceleration. In this paper, we develop hardware accelerator designs for the STRIKE algorithm. Results indicate that the weighted STRIKE accelerator execution times are about 10x longer than the unweighted STRIKE accelerator execution times. To further accelerate the performance of the weighted STRIKE, a parallel module accelerator organization duplicating the weighted STRIKE modules is introduced, achieving near linear speedups for long sequences of 100 or more characters. As demonstrated by Verilog simulations and FPGA runs, the weighted STRIKE module accelerator exhibits three orders of magnitude speed improvement over multi-core and cluster computers. Much higher speedups are possible with the parallel module accelerator.

Published
2022

16. Tracking System with Re-identification Using a RGB String Kernel

Author

Mahboubi, Amal, Brun, Luc, Conte, Donatello, Foggia, Pasquale, Vento, Mario, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fränti, Pasi, editor, Brown, Gavin, editor, Loog, Marco, editor, Escolano, Francisco, editor, and Pelillo, Marcello, editor

Published
2014
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17. Building a Vietnamese SentiWordNet Using Vietnamese Electronic Dictionary and String Kernel

Author

Vu, Xuan-Son, Song, Hyun-Je, Park, Seong-Bae, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Kim, Yang Sok, editor, Kang, Byeong Ho, editor, and Richards, Deborah, editor

Published
2014
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18. Combined Methodology Based on Kernel Regression and Kernel Density Estimation for Sign Language Machine Translation

Author

Boulares, Mehrez, Jemni, Mohamed, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zeng, Zhigang, editor, Li, Yangmin, editor, and King, Irwin, editor

Published
2014
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19. Efficient List-Based Computation of the String Subsequence Kernel

Author

Bellaouar, Slimane, Cherroun, Hadda, Ziadi, Djelloul, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Dediu, Adrian-Horia, editor, Martín-Vide, Carlos, editor, Sierra-Rodríguez, José-Luis, editor, and Truthe, Bianca, editor

Published
2014
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21. Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction.

Author

Singh, Ritambhara, Lanchantin, Jack, Robins, Gabriel, and Qi, Yanjun

Abstract

Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called "Transfer String Kernel" (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on 14 different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of-the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions. [ABSTRACT FROM AUTHOR]

Published
2019
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22. Concept Similarity Based Academic Tweet Community Detection Using Label Propagation

Author

Manju, G., Geetha, T. V., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Prasath, Rajendra, editor, and Kathirvalavakumar, T., editor

Published
2013
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23. Topic Sequence Kernel

Author

Xu, Jian, Lu, Qin, Liu, Zhengzhong, Chai, Junyi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hou, Yuexian, editor, Nie, Jian-Yun, editor, Sun, Le, editor, Wang, Bo, editor, and Zhang, Peng, editor

Published
2012
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24. Factor and Subsequence Kernels and Signatures of Rational Languages

Author

Amarni, Ahmed, Lombardy, Sylvain, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Moreira, Nelma, editor, and Reis, Rogério, editor

Published
2012
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25. Fast q-gram Mining on SLP Compressed Strings

Author

Goto, Keisuke, Bannai, Hideo, Inenaga, Shunsuke, Takeda, Masayuki, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Grossi, Roberto, editor, Sebastiani, Fabrizio, editor, and Silvestri, Fabrizio, editor

Published
2011
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26. Text Classification: A Sequential Reading Approach

Author

Dulac-Arnold, Gabriel, Denoyer, Ludovic, Gallinari, Patrick, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Clough, Paul, editor, Foley, Colum, editor, Gurrin, Cathal, editor, Jones, Gareth J. F., editor, Kraaij, Wessel, editor, Lee, Hyowon, editor, and Mudoch, Vanessa, editor

Published
2011
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27. Large-Scale Training of SVMs with Automata Kernels

Author

Allauzen, Cyril, Cortes, Corinna, Mohri, Mehryar, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Domaratzki, Michael, editor, and Salomaa, Kai, editor

Published
2011
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28. Sparse Substring Pattern Set Discovery Using Linear Programming Boosting

Author

Kashihara, Kazuaki, Hatano, Kohei, Bannai, Hideo, Takeda, Masayuki, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Pfahringer, Bernhard, editor, Holmes, Geoff, editor, and Hoffmann, Achim, editor

Published
2010
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29. A Novel Composite Kernel for Finding Similar Questions in CQA Services

Author

Wang, Jun, Li, Zhoujun, Hu, Xia, Hu, Biyun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chen, Lei, editor, Tang, Changjie, editor, Yang, Jun, editor, and Gao, Yunjun, editor

Published
2010
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30. Leveraging Sequence Classification by Taxonomy-Based Multitask Learning

Author

Widmer, Christian, Leiva, Jose, Altun, Yasemin, Rätsch, Gunnar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, and Berger, Bonnie, editor

Published
2010
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34. Video Event Classification Using Bag of Words and String Kernels

Author

Ballan, Lamberto, Bertini, Marco, Del Bimbo, Alberto, Serra, Giuseppe, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Foggia, Pasquale, editor, Sansone, Carlo, editor, and Vento, Mario, editor

Published
2009
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38. A New Type of Feature – Loose N-Gram Feature in Text Categorization

Author

Zhang, Xian, Zhu, Xiaoyan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martí, Joan, editor, Benedí, José Miguel, editor, Mendonça, Ana Maria, editor, and Serrat, Joan, editor

Published
2007
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40. Stem Kernels for RNA Sequence Analyses

Author

Sakakibara, Yasubumi, Asai, Kiyoshi, Sato, Kengo, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Hochreiter, Sepp, editor, and Wagner, Roland, editor

Published
2007
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43. Text Based Dialog Act Classification for Multiparty Meetings

Author

Zimmermann, Matthias, Hakkani-Tür, Dilek, Shriberg, Elizabeth, Stolcke, Andreas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Renals, Steve, editor, Bengio, Samy, editor, and Fiscus, Jonathan G., editor

Published
2006
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44. Self-organising Map Techniques for Graph Data Applications to Clustering of XML Documents

Author

Tsoi, A. C., Hagenbuchner, M., Sperduti, A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Li, Xue, editor, Zaïane, Osmar R., editor, and Li, Zhanhuai, editor

Published
2006
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45. Evolutionary Construction of Granular Kernel Trees for Cyclooxygenase-2 Inhibitor Activity Comparison

Author

Jin, Bo, Zhang, Yan-Qing, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, Hu, Xiaohua, editor, Pan, Yi, editor, and Lin, Tsau Young, editor

Published
2006
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46. Genetic Granular Kernel Methods for Cyclooxygenase-2 Inhibitor Activity Comparison

Author

Jin, Bo, Zhang, Yan-Qing, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Yi, Zhang, editor, Zurada, Jacek M., editor, Lu, Bao-Liang, editor, and Yin, Hujun, editor

Published
2006
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47. Phase Transitions in Gene Knockdown Networks of Transitive RNAi

Author

Qiu, Shibin, Lane, Terran, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Alexandrov, Vassil N., editor, van Albada, Geert Dick, editor, Sloot, Peter M. A., editor, and Dongarra, Jack, editor

Published
2006
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48. Image Classification Via LZ78 Based String Kernel: A Comparative Study

Author

Li, Ming, Zhu, Yanong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ng, Wee-Keong, editor, Kitsuregawa, Masaru, editor, Li, Jianzhong, editor, and Chang, Kuiyu, editor

Published
2006
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49. Multi-label Text Categorization Using K-Nearest Neighbor Approach with M-Similarity

Author

Feng, Yi, Wu, Zhaohui, Zhou, Zhongmei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Consens, Mariano, editor, and Navarro, Gonzalo, editor

Published
2005
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50. String Kernels of Imperfect Matches for Off-target Detection in RNA Interference

Author

Qiu, Shibin, Lane, Terran, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sunderam, Vaidy S., editor, van Albada, Geert Dick, editor, Sloot, Peter M. A., editor, and Dongarra, Jack J., editor

Published
2005
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