Latent Semantic Kernels

被引：10

作者：

Nello Cristianini

John Shawe-Taylor

Huma Lodhi

机构：

[1] University of London,Department of Computer Science, Royal Holloway

来源：

Journal of Intelligent Information Systems | 2002年 / 18卷

关键词：

Kernel methods; latent semantic indexing; latent semantic kernels; Gram-Schmidt kernels; text categorization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Kernel methods like support vector machines have successfully been used for text categorization. A standard choice of kernel function has been the inner product between the vector-space representation of two documents, in analogy with classical information retrieval (IR) approaches.

引用

页码：127 / 152

页数：25

共 22 条

[1]

Aizerman M.(1964)Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning Automation and Remote Control 25 821-837

[2]

Braverman E.(1990)Indexing by Latent Semantic Analysis Journal of the American Society for Information Science 41 391-407

[3]

Rozonoer L.(2002)Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? Machine Learning 46 423-444

[4]

Deerwester S.(1975)A Vector Space Model for Information Retrieval Journal of the American Society for Information Science 18 613-620

[5]

Dumais S.T.(2001)SV Estimating the Support of a Higher Dimensional Distribution, Neural Computation Neural Information Processing Systems 13 1443-1471

[6]

Furnas G.W.(1998)Structural Risk Minimization over Data-Dependent Hierarchies IEEE Transactions on Information Theory 44 1926-1940

[7]

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[10]

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