P. Domingos and M. J. Pazzani. On the the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2-3):103--130, 1997.  Home/Search Document Details and Download
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A Comparative Study of Discretization Methods for Naive-Bayes.. - Yang, Webb (Correct)
....the probability by: Classifiers using (4) are called naive Bayes classifiers. Naive Bayes classifiers are simple, e#cient and robust to noisy data. One limitation is that the attribute independence assumption in (3) is often violated in the real world. However, Domingos and Pazzani [8] suggest that this limitation has less impact than might be expected because classification under zero one loss is only a function of the sign of the probability estimation; the classification accuracy can remain high even while the probability estimation is poor. 3 Discretization for Naive Bayes ....
Domingos, P., and Pazzani, M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997), 103--130.
Computing Conditional Probabilities In Large Domains By Maximizing.. - III (2003) (1 citation) (Correct)
....computing 2.48, NB has attracted a lot of attention. Surprisingly, the results using NB have been shown to outperform many more sophisticated algorithms on classification tasks. Recently, it has been shown that even if the independent assumption for X E is violated, NB classifiers can be optimal [16]. Why does NB do so well While this question has been debated, it is understood that NB captures many of the low order interactions between variables. In the real world data sets used to test NB, there is a significant trade o# between bias and variance errors. NB classifiers are known to have ....
Pedro Domingos and Michael J. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2-3):103--130, 1997.
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P. Domingos and M. Pazzani. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 29:103--130, 1997.
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P. Domingos and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103--130, 1997.
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P. Domingos and M. Pazzani. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 29:103--130, 1997.
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P. Domingos and M. Pazzani. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 29:103--130, 1997.
Learning to Map between Ontologies on the Semantic Web - Doan, Madhavan, Domingos.. (2002) (62 citations) Self-citation (Domingos) (Correct)
....of the instance . For example, the textual content of the instance Professor Cook is R. Cook, Ph.D. University of Sidney, Australia . The textual content of the instance CSE 342 is the text content of this course homepage. The Content Learner employs the Naive Bayes learning technique [13], one of the most popular and e#ective text classification methods. It treats the textual content of each input instance as a bag of tokens, which is generated by parsing and stemming the words and symbols in the content. Let d = w1 , wk be the content of an input instance, However, ....
....instances that belong to A, and n(w j , A) is the number of times token w j appears in all training instances belonging to A. Even though the independence assumption is typically not valid, the Naive Bayes learner still performs surprisingly well in many domains, notably text based ones (see [13] for an explanation) We compute P (A d) in a similar manner. Hence, the Content Learner predicts A with probability P (A d) and A with the probability P (A d) The Content Learner works well on long textual elements, such as course descriptions, or elements with very distinct and descriptive ....
P. Domingos and M. Pazzani. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 29:103--130, 1997.
MetaCost: A General Method for Making Classifiers Cost-Sensitive - Domingos (1999) (44 citations) Self-citation (Domingos) (Correct)
....pattern recognition, neura networks and other areas for several decades. As a result, many well developed approaches to it now exist, including rule induction [20, 12] decision tree induction [8, 23] instance based learning [11, 1] linear and neural classifiers [3] Bayesian learning [17, 16], and others. In classification problems, the goal is to correctly assign examples (typically described as vectors of attributes) to one of a finite number of classes. Most of the currently available algorithms for classification are designed to minimize zero one loss or error rate: the number of ....
.... is important only insofar as it influences the final frontiers produced; probability estimates can be quite poor and still lead to optimal classification, as long as the class that minimizes conditionoJ risk given the estimated probabilities is the same that minimizes it given the true ones [16]. One possibility would be to use standard probability estimation techniques, such as kernel density estimation [17] However, successful learning of a cost sensitive classifier using this approach would require that the machine learning bias (i.e. the implicit assumptions) of both the classifier ....
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P. Domingos and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103-130, 1997.
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