Drucker, H., Wu, D., and Vapnik, V. (1999). Support vector machines for spam categorization. IEEE Trans. on Neural Networks, 10(5):1048-1054. Home/Search Document Not in Database Summary Related Articles Check |
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Towards An Adaptive Mail Classifier - Manco, Masciari, Ruffolo, Tagarelli (2002) (Correct)
.... be approached by building a classifier system capable of minimizing three main measures: error rate, false positive rate, and false negative rate [27] Among the techniques used for classifying spam messages, we mention the Rocchio approach (and similar approaches based on Support Vector Machines [7]) Papers [4] and [13] describe two rule based systems exploiting text mining techniques for the classification of e mail correspondence. These approaches differ mainly in the preprocessing phase: in the first approach a simple boolean vector model is used; on the other side, 13] proposes a ....
H. Drucker, D. Wu, and V. N. Vapnik. Support Vector Machines for Spam Categorization. IEEE Transactions on Neural networks, 10(5), 1999.
Boosting Trees for Anti-Spam Email Filtering - Carreras, Marquez (2001) (13 citations) (Correct)
....Learning [19] Spam filtering has also been treated as a particular case of TC. Cohen [3] used a method based on TF IDF weighting and the rule learning algorithm RIPPER to classify and filter email. Sahami et al. 12] used the Naive Bayes approach to filter spam email. Drucker et al. [5] compared Support Vector Machines (SVM) boosting of C4.5 trees, RIPPER and Rocchio, concluding 2 that SVM s and boosting are the top performing methods and suggesting that SVM s are slightly better in distinguishing the two types of misclassification. Androutsopoulos and colleagues compared ....
....clearly outperform the other algorithms. Naive Bayes, k NN and the Stack achieve precision rates slightly lower than 10 PU1 corpus 55 60 65 70 75 80 85 90 95 100 1 5 25 100 5001000 2500 F1 Number of rounds Stumps TreeBoost[1] TreeBoost[2] TreeBoost[3] TreeBoost[4] TreeBoost[5] Naive Bayes Decision Trees LingSpam corpus 55 60 65 70 75 80 85 90 95 100 1 5 25 100 500 1000 F1 Number of rounds Stumps TreeBoost[1] TreeBoost[2] TreeBoost[3] TreeBoost[4] TreeBoost[5] Naive Bayes k NN Stacking Figure 1: F 1 measure of Stumps and TreeBoost[d] for ....
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H. Drucker, D. Wu, and V.N.Vapnik. Support vector machines for spam categorization. IEEE Trans. on Neural Networks, 10(5):1048-- 1054, 1999.
Multi-class Text Categorization with Error Correcting Codes - Kindermann, Leopold, Paass (2000) (Correct)
....Dumais et al. DPHS98] use linear SVM for text categorization because they are both accurate and fast. They are 35 times faster to train than the next most accurate (a decision tree) of the tested classifiers. They apply SVM to the Reuters 21578 collection, e mails and web pages. Drucker at al. [DWV99] classify e mails as spam and non spam. They find that boosting trees and SVM have similar performance in terms of accuracy and speed, but SVM train significantly faster. 2.3 Transformations of frequency vectors As lexical units scale to different order of magnitude in larger documents, it is ....
H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048--1054, 1999.
Authorship Attribution with Support Vector Machines - Diederich, Kindermann.. (2000) (13 citations) (Correct)
....Dumais et al. DPHS98] use linear SVMs for text categorization because they are both accurate and fast. They are 35 times faster to train than the next most accurate (a decision tree) of the tested classifiers. They applied SVMs to the Reuter 21578 collection, emails and web pages. Drucker at al. [DWV99] classify emails as spam and non spam. They find that boosting trees and SVMs have similar performance in terms of accuracy and speed. SVMs train significantly faster. 6 4 Transformations of Frequency Vectors 4.1 Normalization of Length It is a well known fact that the frequency distribution ....
H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048--1054, 1999.
Multi-class Classification with Error Correcting Codes - Kindermann, Leopold, Paass (Correct)
....Dumais et al. DPHS98] use linear SVM for text categorization because they are both accurate and fast. They are 35 times faster to train than the next most accurate (a decision tree) of the tested classifiers. They apply SVM to the Reuters21578 collection, e mails and web pages. Drucker at al. [DWV99] classify e mails as spam and non spam. They find that boosting trees and SVM have similar performance in terms of accuracy and speed, but SVM train significantly faster. Table 3 Table of tested combinations of frequency transformations and SVM kernels # abbreviation coding; kernel 1 relFreq0 ....
H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048-- 1054, 1999.
Relevance Feedback using Support Vector Machines - Drucker, Shahraray, Gibbon (2001) (2 citations) Self-citation (Drucker) (Correct)
....are returned. These feedback iterations continue until the user terminates the procedure. We first concentrate on the state when between one and nine (inclusive) relevant documents are returned in the initial screen. Our method will be based on the use of support vector machines (Vapnik, 1998; Drucker, 1999; Joachims, 1998) with comparisons to other RF techniques: Rocchio (1971) Ide regular and Ide dec hi (Salton and Buckley, 1990; Harman, 1992) These algorithms will be examined in detail later but suffice to say now that all except Ide dec hi use all the relevant and non relevant documents on the ....
....1.2 Techniques Investigated. One difference between our study and others is the simultaneous tracking of performance as a function of feedback iteration and the use of SVM s. Although there have been many studies of the use of SVM s in text retrieval (Joachims, 1998; Vapnik, 1998, 1992; Drucker, 1999), most studies emphasize finding the method that optimizes performance after one feedback iteration. SVM s have been studied in the context of the IR filtering problem (Dumais, 1998; Joachims, 1998) It is understood that both RF and filtering problems are both classification problems in that ....
[Article contains additional citation context not shown here]
Drucker, H., Wu, D., & Vapnik, V.N. (1999). Support Vector Machines for Spam Categorization, IEEE Transactions on Neural Networks, 10, 1048-1054.
Vicinal Risk Minimization - Chapelle, Weston, Bottou, Vapnik (2001) (3 citations) Self-citation (Vapnik) (Correct)
....situation arises naturally in anomaly detection systems. This also occurs often in recognition systems that reject invalid patterns by defining a garbage class for grouping all ambiguous or unrecognizable cases. Although there are successful non generative approaches (Schuurmans and Southey, 2000) (Drucker, Wu and Vapnik, 1999), the generative framework is undeniably appealing. Recent results (Jaakkola, Meila and Jebara, 2000) even define generative models that contain SVM as special cases. This paper discusses the Vicinal Risk Minimization (VRM) principle, summarily introduced in (Vapnik, 1999) This principle was ....
Drucker, H., Wu, D., and Vapnik, V. (1999). Support vector machines for spam categorization. Neural Networks, 10:1048--1054.
The Maximum-Margin Approach to Learning Text Classifiers -.. - Joachims (2000) (17 citations) (Correct)
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Drucker, H., Wu, D., and Vapnik, V. (1999). Support vector machines for spam categorization. IEEE Trans. on Neural Networks, 10(5):1048-1054.
Filtron: A Learning-Based Anti-Spam Filter - Eirinaios Michelakis Ion (2004) (Correct)
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H. D. Drucker, D. Wu, and V. Vapnik. Support Vector Machines for spam categorization. IEEE Transactions On Neural Networks, 10(5):1048--1054, 1999.
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H. D. Drucker, D. Wu, and V. Vapnik. Support Vector Machines for spam categorization. IEEE Transactions On Neural Networks, 10(5):1048--1054, 1999.
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H. Drucker, D. Wu, and V. Vapnik, Support vector machines for spam categorization, IEEE Transactions on Neural Networks, 10 (1999), pp. 10481054.
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H. Drucker, D. Wu, and V. N. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048--1054, 1999.
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H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Trans. On Neural Networks, 10(5):1048--1054, 1999.
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H. Drucker, D. Wu, and V.N. Vapnik. 1999. Support vector machines for spam categorization. IEEE Trans. On Neural Networks, 10(5):1048--1054.
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H. Drucker, D. Wu, and V. Vapnik. Support vector machines for Spam categorization. IEEE-NN, 10(5):1048--1054, 1999.
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Harris Drucker, Donghui Wu, and Vladimir N. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10:1048--1054, 1999.
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H. Drucker, D. Wu, and V.N.Vapnik, "Support vector machines for spam categorization.," IEEE Trans. on Neural Networks, vol. 10, pp. 1048-1054, 1999.
Filtron: A Learning-Based Anti-Spam Filter - Eirinaios Michelakis Ion (2004) (Correct)
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H. D. Drucker, D. Wu, and V. Vapnik. Support Vector Machines for spam categorization. IEEE Transactions On Neural Networks, 10(5):1048--1054, 1999.
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Drucker, H., Wu, D., and Vapnik, V., Support vector machines for spam categorization, IEEE Trans Neural Network, 10:1048--1054, 1999.
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H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5), 1999.
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Drucker, H., Wu, D., Vapnik, V. (1999): Support Vector Machines for Spam Categorization. In IEEE Trans. on Neural Networks , vol 10 (5). pp. 1048-1054.
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H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. IEEE Trans. Neural Networks, 10(5):1048--1054, 1999.
Text Categorization - Sebastiani (2005) (2 citations) (Correct)
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Drucker, H., Vapnik, V. & Wu, D., Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5), pp. 1048--1054, 1999.
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Drucker H., with Wu D. and Vapnik V. Support vector machines for spam categorization. IEEE Trans. on Neural Networks, 10, p. 1048-1054. 1999.
A Novel Method of Protein Secondary Structure Prediction with.. - Hua, Sun (2001) (5 citations) (Correct)
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Drucker, H., Wu, D. & Vapnik, V. (1999). Support vector machines for spam categorization. IEEE Trans. on Neural Networks, ##, 1048-1054.
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