# Trends & Controversies: Support Vector Machines

@article{Hearst1998TrendsC, title={Trends \& Controversies: Support Vector Machines}, author={Marti A. Hearst}, journal={IEEE Intell. Syst.}, year={1998}, volume={13}, pages={18-28} }

My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from…

## 2,674 Citations

### Using support vector machines for automatic new topic identification

- Computer ScienceASIST
- 2007

Findings of this study suggest that support vector machines' performance depends on the characteristics of the dataset it is applied on.

### Performance Analysis of Naiotave Bayes Classification, Support Vector Machines and Neural Networks for Spam Categorization

- Computer ScienceWSC
- 2004

The performances of Support Vector Machines, Neural Networks, and Naive Bayes techniques are compared using a publicly available corpus (LINGSPAM) for different cost scenarios and the results show that NN has significantly better performance than the two other, having acceptable training times.

### Using wavelet analysis for text categorization in digital libraries: a first experiment with Strathprints

- Computer ScienceInternational Journal on Digital Libraries
- 2012

A pilot experiment of replacing standard test collections by a set of 6,000 objects from a real-world digital repository, indexed by Library of Congress Subject Headings, and test support vector machines in a supervised learning setting for their ability to reproduce the existing classification suggests that wavelet-based kernels slightly outperformed traditional kernels on classification reconstruction from abstracts and vice versa from full-text documents.

### Fringe SVM Settings and Aggressive Feature Reduction

- Computer Science
- 2003

Statistical techniques for aggressive feature reduction are studied on data obtained in agene knock-out experiment and it is shown that relatively superior performance of fringe SVMs can be observed on regular textmining data bases, such as Reuters newswire benchmark, if only the less frequent features are used.

### ML-SPD: Machine Learning based Sentiment Polarity Detection

- Computer Science2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
- 2020

Different text representation models are explored in conjunction with state-of-the-art traditional Machine Learning techniques: Support Vector Machines (SVM), Neural Networks (NN), Nave Bayes (NB), and combination of NB and SVM classifier (NBSVM) to solve SPD problem.

### On Text-based Mining with Active Learning and Background Knowledge Using SVM

- Computer ScienceSoft Comput.
- 2007

Experimental results show that the proposed techniques present a considerable improvement in classification performance, even when small labeled training sets are available.

### English & Persian Document Categorization based on a Novel Hybrid Algorithm

- Computer Science
- 2012

Experimental evaluation of automatic document categorization based on large text hierarchy by taking into account hierarchical structure of examples and using feature selection for large text data shows that this approach gives promising results and can potentially be used for document classification on the Web.

### E-cient Pattern Selection for Support Vector Classiflers and its CRM Application

- Computer Science
- 2004

This thesis proposes neighborhood property based pattern selection algorithm (NPPS) which selects the patterns near the decision boundary based on the neighborhood properties which is implemented as a naive form with time complexity O(M) where M is the number of given training patterns.

### Do We Need More Training Samples For Text Classification?

- Computer ScienceAICCC '18
- 2018

In this article, feature selection as a technology to remove irrelevant features was found be able to prevent overfitting to a great extent and the kappa coefficient as a performance measure of classifiers could increase 11 points at the maximum.

### Provably Fast Training Algorithms for Support Vector Machines

- Computer ScienceProceedings 2001 IEEE International Conference on Data Mining
- 2001

An upper bound on the expected running time is formally proved which is quasilinear with respect to the number of data points and polynomial withrespect to the other parameters, i.e., the number and inverse of a chosen soft margin parameter.

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