Nigam, K., & Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. Proc. of Information and Knowledge Management (pp. 86--93). Home/Search Document Details and Download
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....This suggests a straightforward application of semantic mapping evaluating search engines. In machine learning, semi supervised methods are techniques for extracting knowledge from data when only a very small fraction of the data is labeled so as to provide examples to the learning system [3, 28]. Typically in supervised learning a sufficient number of labeled examples are available (the training set) When a training set is not available, a few examples can be labeled by hand and then more examples can be labeled automatically by a bootstrapping process. Finally the resulting ....
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....data and then repeating anew the labeling of the originally unlabeled data. This approach yielded classification results that exceed those obtained without the extra unlabeled data. Blum and Mitchell s [3] co training algorithm also applies to cases where there is a source of unlabeled data [13], only in cases where the target concept can be described in two redundant ways (such as through two different subsets of attributes describing each example) Each view of the data is used to create a predictor, and each predictor is used to classify unlabeled data. The data labeled by one ....
K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of the Ninth International Conference on Information and Knowledge Management, 2000.
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.... approaches include using Expectation Maximization to estimate maximum a posteriori parameters of a generatire model [12] using a generatire model built from unlabeled data to perform discriminative classification [5] using redundant feature splits for bootstrapping classifiers with co training [1, 11] and using transductive inference for support vector machines to optimize performance on a specific test set [6] These results have shown that using unlabeled data can significantly decrease classification error, especially when labeled training data are sparse. For the case of textual data in ....
K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of cotraining. In Proceedings of CIIM, 2000.
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....to the web page. Blum and Mitchell (1998) show that under certain theoretical assumptions, a weak learner can be arbitrarily improved given sufficient unlabeled examples. They also present the co training algorithm that iteratively selects an unlabeled example, gives it a label, and relearns. Nigam and Ghani (2000) argue that the co training algorithm and its variants succeed in part because they are more robust to the assumptions of their underlying classifier representations. Collins and Singer (1999) present a boosting based algorithm, coBoost, for learning in the co training setting; it tries to ....
Nigam, K., & Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. Ninth International Conference on Information and Knowledge Management, pp. 86--93.
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Nigam, K., & Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. Proc. of Information and Knowledge Management (pp. 86--93).
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Kamal Nigam and Rayid Ghani. 2000. Analyzing the effectiveness and applicability of co-training. In Proc. of Ninth International Conference on Information and Knowledge (CIKM-2000).
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