Joachims, T.: Learning to Classify Text using Support Vector Machines. Volume 668 of Kluwer International Series in Engineering and Computer Science. Kluwer (2002) Home/Search Document Not in Database Summary Related Articles Check |
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Frequent-Subsequence-Based Prediction of Outer - Membrane Proteins Rong (Correct)
....precision is reduced whereas recall is increased to the 60 level. However, precision can still Optimal Hyperplane Support vectors M w be maintained at the 90 level. This shows that the idea of refinement works quite well, as expected. 6. 2 SVM Classification The SVM implementation [9] was chosen because it is quite well known and has been used extensively in previous research. Since an SVM can be trained with many different choices of input parameters, we have carried out separate experiments to investigate the impact of the MinSup parameter and the SVM parameters. 6.2.1 ....
Joachims T., Learning to Classify Text Using Support Vector Machines. Dissertation, Kluwer, 2002. software downloadable at http://svmlight.joachims.org/
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....to easily modify the system by changing the learning component. As an experiment, we replaced the learner in aBwi with three other learning systems: Slipper (with its default options, including mechanisms to handle noisy data and internal cross validation to set T) Ripper [20] and SVM Light [21]. Some representative results are shown in Figure 2 under the method name NrBwi. On this problem, none of the other learners improves over aBwi, although all outperform the hand coded systems. Classification results. As discussed above, for some purposes it is su#cient to simply identify ....
Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer, 2002.
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....We formulate the problem as iterative repair problem with a number of repairs limited by the size of the respective instance. Since this problem can be interpreted as acyclic search problem, we are capable of using the particularly e#cient rout algorithm of reinforcement learning [3] and SVM [5] for approximating the value function, achieving a very sparse representation of the respective training set and good generalization ability. We demonstrate the ability of the approach to improve the initial greedy strategy even after few training steps, and we investigate the generalization ....
....check when adding new training patterns to T , deleting old patterns if they almost coincide with new ones. As already mentioned, f RDF is given by a SVM trained on T . The SVM constitutes a universal learning algorithm for functions between real vector spaces with polynomial training complexity [4, 5, 10]. Since the SVM aims at minimizing the structural risk directly, we can expect very good generalization ability even for few training patterns. Moreover, the SVM is determined by the support vectors which constitute a sparse subset of T , hence allowing us to keep the size of T nearly constant. In ....
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T.Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer, 2002.
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....for the active target class among the k closest training instances; for SVMs we picked thresholds on the distance from the optimal hyperplane; for SBC we chose thresholds on the probability of the active class, and for NNs on the activation level of the output unit. We used the SVMlight library [11], the NN toolbox of Matlab [15] and our own Matlab implementations for the remaining methods. 4.4 Experimental Results Table 3 shows the relative performance of all IAMB variants for all classifiers and feature selection methods. Table 4 summarizes the relative performance in terms of time of ....
Joachims T. Learning to classify text using support vector machines. Dissertation, Kluwer, 2002.
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Joachims, T.: Learning to Classify Text using Support Vector Machines. Volume 668 of Kluwer International Series in Engineering and Computer Science. Kluwer (2002)
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T. Joachims. Learning to Classify Text using Support Vector Machines. Kluwer International Series in Engineering and Computer Science. Kluwer, 2002.
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T. Joachims. Learning to Classify Text using Support Vector Machines, volume 668 of Kluwer International Series in Engineering and Computer Science. Kluwer, 2002.
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Joachims, T.: 2002a, Learning to Classify Text using Support Vector Machines, Vol. 668 of Kluwer International Series in Engineering and Computer Science. Kluwer.
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T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms: Kluwer Academic Publishers, 2002.
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Joachims, T. (2002), Learning to Classify Text using Support Vector Machines. Kluwer Academic Press, Dordrecht.
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Joachims, T. (2002), Learning to Classify Text using Support Vector Machines. Kluwer Academic Press, Dordrecht.
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Joachims, T. (2002). Learning to classify text using support vector machines: Methods, theory, and algorithms. Kluwer.
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Joachims, T. 2002. Learning to Classify Text Using Support Vector Machines. Kluwer.
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T. Joachims. Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers, 2002.
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Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers (2002)
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Joachims, T.: Learning to Classify Text using Support Vector Machines. Volume 668 of Kluwer International Series in Engineering and Computer Science. Kluwer (2002)
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T. Joachims. Learning to Classify Text using Support Vector Machines. Kluwer Academic Publishers, Dordrecht, NL, 2002.
A Benchmark Dataset for Audio Classification and.. - Homburg, Mierswa.. (2005) (Correct)
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T. Joachims. Learning to Classify Text using Support Vector Machines. Kluwer International Series in Engineering and Computer Science. Kluwer, 2002.
Method Trees: Building Blocks for Self-Organizable.. - Mierswa, Morik (2005) (Correct)
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T. Joachims. Learning to Classify Text using Support Vector Machines, volume 668 of Kluwer International Series in Engineering and Computer Science. Kluwer, 2002.
Automatic Feature Extraction for Classifying Audio Data - Mierswa, Morik (2005) (Correct)
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Joachims, T.: 2002a, Learning to Classify Text using Support Vector Machines, Vol. 668 of Kluwer International Series in Engineering and Computer Science. Kluwer.
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Joachims, T. Learning to Classify Text Using Support Vector Machines. Kluwer, 2002.
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Joachims, T. Learning to Classify Text using Support Vector Machines. Kluwer, 2002.
Using Data and Text Mining Techniques for Yeast.. - Krogel, Denecke.. (2002) (Correct)
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T. Joachims. Learning to Classify Text using Support Vector Machines. Kluwer, 2002.
Improving iterative repair strategies for scheduling with the.. - Gersmann, Hammer (Correct)
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T.Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer, 2002.
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