Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?

By Edda Leopold, Jörg Kindermann. Machine-mediated learning, 2002

493 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/88151be6a62a5843d7ff0b131452f8b3c6491cb2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Francis R. Bach
2. Huan Liu
3. Ping Li
4. John Shawe-Taylor
5. Lei Yu
6. Alessandro Moschitti
7. Nello Cristianini
8. Charu C. Aggarwal

In Information Systems

1. Ping Li
2. Huan Liu
3. Lei Yu
4. Roberto Perdisci
5. Wenke Lee
6. John Shawe-Taylor
7. Rada Mihalcea
8. Samer Hassan

Citations in the corpus

• Random-Walk Term Weighting for Improved Text Classification (2007, methodology): “itf : defined as one minus the inverse term frequency, was found to have an excellent performance when used with an SVM linear kernel [14, 6].”
• Very sparse random projections (2006, methodology): “For example, as shown in [23, 26], in text categorization using support vector machine (SVM), choosing an appropriate term weighting scheme is far more important than tuning kernel functions of SVM.”
• Latent Semantic Kernels (2001, methodology): “Several methods have been proposed, and it is known that this has a strong influence on generalisation (Leopold et al., 2002).”
• A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011, unknown): “Additional feature and kernel optimization for the SVM may improve its performance.(35) However, it also indicates the complexity of SVM parameter optimization.”
• A Brief Survey of Text Mining (2005, methodology): “A Support Vector Machine (SVM) is a supervised classification algorithm that recently has been applied successfully to text classification tasks [Joa98, DPHS98, LK02 ].”
• Supervised and Traditional Term Weighting Methods for Automatic Text Categorization (2009, unknown): “Inspired by the inverse document frequency, inverse term frequency (ITF) was presented in [7].”
• Toward integrating feature selection algorithms for classification and clustering (2005, background): “…[40], [63], [81], [90], machine learning [6], [41], [43], [44], and data mining [17], [18], [42], and widely applied to many fields such as text categorization [50], [70], [94], image retrieval [77], [86], customer relationship management [69], intrusion detection [49], and genomic analysis [91].”
• Fast Kernels for String and Tree Matching (2002, unknown): “, for inverse document frequency counting [11].”