Using support vector machines in data mining
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Summary
Support Vector Machines (SVS) are a recent addition to the family of multivariate data analysis and have the potential to improve data analysis.
- Type
- article
- Published
- 2004-12-17
- Cited by
- 5
- References
- 23
- OpenAlex
- https://openalex.org/W1869764775
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15344450
Keywords
Support vector machine, Computer science, Data mining, Multivariate statistics, Multivariate analysis
References
- Support Vector Clustering
- SCOP: a structural classification of proteins database for the investigation of sequences and structures.
- Identification of common molecular subsequences.
- A training algorithm for optimal margin classifiers
- An introduction to kernel-based learning algorithms
- Multiple Alignment Using Hidden Markov Models
- Hidden Markov models for detecting remote protein homologies
- Handwritten Digit Recognition with a Back-Propagation Network
- Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors
- Combining pairwise sequence similarity and support vector machines for remote protein homology detection
- Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
- Computationally efficient face detection
- A geometrical representation of McCulloch-Pitts neural model and its applications
- The Set Covering Machine
- A Discriminative Framework for Detecting Remote Protein Homologies
- The Nature of Statistical Learning Theory
- The Nature of Statistical Learning Theory
- Fast Training of Support Vector Machines using Sequential Minimal Optimization
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