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Support Vector Machine
Related Publications
(51)
A comparison of two learning algorithms for text categorization
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Learning to Classify Text using Support Vector Machines
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Learning to Classify Text using Support Vector Machines
(
Citations: 376
)
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Thorsten Joachims
Published in 2002.
Cumulative
Annual
Citation Context
(235)
...focus on SVMs only, since it has been shown that SVMs outperform other conventional learning methods for the task of text classification [13][14][15][16][
17
]...
Elena Hensinger
,
et al.
Learning Readers' News Preferences with Support Vector Machines
...a linear kernel and default parameters [
27
]...
Achim Klein
,
et al.
Extracting Investor Sentiment from Weblog Texts: A Knowledge-based App...
...T. Joachims [
9
] classified documents into categories by using SVM and obtained better results than those obtained by using other machine learning techniques such as Bayes and K-NN...
Fouzi Harrag
,
et al.
Stemming as a feature reduction technique for Arabic Text Categorizati...
...feature vector, each region can be classified to its semantic entity class via SVM algorithm [
23
, 41]...
Hangzai Luo
,
et al.
Multimedia news exploration and retrieval by integrating keywords, rel...
...In text classification tasks, each document is typically represented by a BoW or some other similar scheme [
4
]...
...A BoW representation of a document consists in a high-dimensional vector containing some measure, like the term-frequency (TF) or the term-frequency inverse-documentfrequency (TF-IDF) of a term (or word) [
4
]...
...Each document is represented by a single vector, which is usually sparse, since many of its features are zero (each document only contains a small subset of the available terms) [
4
]...
...Let X be the p × n term-document (TD) matrix representing D; each column of X corresponds to a document, whereas each row corresponds to a term (e.g., a word); each column is the BoW representation of a document [
4
], [5]...
...SVM classifiers have been found very effective for BoW-based text classification [
4
], [6], [8]...
Artur Ferreira
,
et al.
Efficient unsupervised feature selection for sparse data
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(376)
The use of hybrid manifold learning and support vector machines in the prediction of business failure
(
Citations: 4
)
Fengyi Lin
,
Ching-Chiang Yeh
,
Meng-Yuan Lee
Journal:
Knowledge Based Systems - KBS
, vol. 24, no. 1, pp. 95-101, 2011
A support vector machine-based model for detecting top management fraud
(
Citations: 4
)
Ping-Feng Pai
,
Ming-Fu Hsu
,
Ming-Chieh Wang
Journal:
Knowledge Based Systems - KBS
, vol. 24, no. 2, pp. 314-321, 2011
Neighboring joint density-based JPEG steganalysis
(
Citations: 1
)
Qingzhong Liu
,
Andrew H. Sung
,
Mengyu Qiao
Published in 2011.
9225 POSTER G-CSF Administration in First Line Chemotherapy With ABVD for Hodgkin's Lymphoma in Adults
M. Pantarotto
,
C. Moreira
,
M. Ferreira
,
L. Lombo
,
D. Pereira
,
J. Faria
,
M. Mariano
,
I. Faustino
,
N. Domingues
,
J. M. Mariz
Journal:
Computational Statistics & Data Analysis - CS&DA
, vol. 47, pp. S646-S646, 2011
Learning Readers' News Preferences with Support Vector Machines
Elena Hensinger
,
Ilias N. Flaounas
,
Nello Cristianini
Conference:
International Conference on Adaptive and Natural Computing Algorithms - ICANNGA
, pp. 322-331, 2011