SVM classification using sequences of phonemes and syllables

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2431 LNAI, Issue , 2002, Pages 373-384

  Paaß, Gerhard ^a   Leopold, Edda ^a   Larson, Martha ^b   Kindermann, Jörg ^a   Eickeler, Stefan ^b  

^a FRAUNHOFER IAIS   (Germany)

^b FRAUNHOFER GESELLSCHAFT   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INFORMATION RETRIEVAL SYSTEMS; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; DOCUMENT CLASSIFICATION; SPOKEN DOCUMENT; SUB-WORD UNITS; SVM CLASSIFICATION; WRITTEN DOCUMENTS;

SUPPORT VECTOR MACHINES; INFORMATION RETRIEVAL SYSTEMS;

EID: 79959723349     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45681-3_31     Document Type: Conference Paper

References (13)

1. Drucker, H, Wu, D., Vapnik, V. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10 (5): 10048-1054, 1999. 374
2. Dumais, S., Platt, J., Heckerman, D., Sahami, M. (1998): Inductive learning algorithms and representations for text categorization. In: 7th International Conference on Information and Knowledge Management, 1998. 374
3. Gelman, A., Carlin J. B., Stern, H. S., Rubin, D. B.: Bayesian Data Analysis. Chapman, Hall, London, 1995. 378
4. Glavitsch, U., Schäuble, P. (1992): A System for Retrieving Speech Documents, SIGIR 1992. 376
5. Haussler, David (1999): Convolution Kernels on Discrete Structures, UCSL-CRL- 99-10. 374
6. Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features. Proc. ECML '98, (pp. 137-142). 373, 374, 379
7. Klabbers, E., Stöber, K., Veldhuis, R. Wagner, P., Breuer, S.: Speech synthesis development made easy: The Bonn Open Synthesis System, EUROSPEECH 2001. 376, 377
8. Larson, M.: Sub-word-based language models for speech recognition: implications for spoken document retrieval, Proc. Workshop on Language Modeling and IR. Pittsburgh 2001. 376
9. Leopold, E., Kindermann, J.: Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? Machine Learning, 46, 2002, 423-444. 374
10. Leslie, Christa, Eskin, Eleazar, Noble, William Stafford (2002): The Spectrum Kernel: A String Kernel SVM Protein Classification. To appear: Pacific Symposium on Biocomputing. 374
11. Lodhi, Huma, Shawe-Taylor, John, Cristianini, Nello &Watkins, Chris (2001) Text classification using kernels, NIPS 2001, pp. 563-569. MIT Press. 374
12. Manning, Christopher D., Schütze (2000): Foundations of Statistical Natural Language Processing, MIT Press. 378
13. Watkins, Chris (1998): Dynamicalign ment Kernels. Technical report, Royal Holloway, University of London. CSD-TR-98-11. 374