Michael Pazzani. Searching for attribute dependencies in Bayesian classifiers. In D. Fisher and H. Lenz, editors, Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pages 424--429, Ft.Lauderdale, FL., 1995. Home/Search Document Not in Database Summary Related Articles Check |
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Computing Conditional Probabilities In Large Domains By Maximizing.. - III (2003) (1 citation) (Correct)
....features be dependent on it, i.e. will it be useful to the network While our complex functions may be any binary function, traditionally only binary functions that can be represented using the And operator are usually considered. Previously, And functions have been used in naive Bayes networks [43, 59]. Within this chapter, we will describe several methods for adding complex feature functions. A 61 couple of these methods will only apply to the RLN since the magnitude of the weights between functions is used. Two methods, which use variable frequency and average error, can be applied to both ....
Michael Pazzani. Searching for attribute dependencies in Bayesian classifiers. In D. Fisher and H. Lenz, editors, Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pages 424--429, Ft.Lauderdale, FL., 1995.
The Wrapper Approach - Kohavi, John (1998) (10 citations) (Correct)
....frontier nodes in parallel in a race, until one node becomes a clear winner. Since the introduction of the wrapper approach (John et al. 1994) several authors have experimented with it in various contexts. Langley and Sage (1994) used the wrapper approach to select features for Naive Bayes. Pazzani (1995) joined features (created super features that compound others) for Naive Bayes using the wrapper approach and showed that it indeed finds correct combinations when features interact. Singh and Provan (1995) used the wrapper approach to select features for Bayesian networks and showed significant ....
Pazzani, M. (1995). Searching for attribute dependencies in Bayesian classifiers.
Scaling Up the Accuracy of Naive-Bayes Classifiers: a.. - Kohavi (1996) (32 citations) (Correct)
....may help, but they cannot increase the representation power as was done here, thus we will not review them. Kononenko (1991) attempted to join pairs of attributes (make a cross product attribute) based on statistical tests for independence. Experimentation results were very disappointing. Pazzani (1995) searched for attributes to join based on cross validation estimates. Recently, Friedman Goldszmidt (1996) showed how to learn a Tree Augmented Naive Bayes (TAN) which is a Bayes network restricted to a tree topology. The results are promising and running times should scale up, but the ....
Pazzani, M. 1995. Searching for attribute dependencies in bayesian classifiers. In Fifth International Workshop on Artificial Intelligence and Statistics, 424--429.
Estimating Continuous Distributions in Bayesian Classifiers - John, Langley (1995) (66 citations) (Correct)
....Bayes that lessen dependence on its assumptions but that retain its inherent simplicity and clear probabilistic semantics. Langley Sage (1994) describe a variation that mitigates the independence assumption by eliminating predictive features that are correlated with others. Kononenko (1991) and Pazzani (1995) propose an alternative response to this assumption by selectively introducing combinations of attributes into the modeling process. These and similar approaches represent an important line of research in machine learning, the goal of which is to discover learning methods that not only work well ....
Pazzani, M. (1995), Searching for attribute dependencies in Bayesian classifiers, in "Fifth International Workshop on Artificial Intelligence and Statistics ", pp. 424--429.
Beyond Independence: Conditions for the Optimality of the.. - Domingos, Pazzani (1996) (109 citations) Self-citation (Pazzani) (Correct)
....Salzberg Aha, 1994; Dougherty, Kohavi Sahami, 1995) but no interpretation of this has been proposed so far. Several extensions of the SBC have been introduced with the goal of increasing its tolerance of attribute dependences (e.g. Kononenko, 1991; Langley, 1993; Langley Sage, 1994; Pazzani, 1995)) usually with moderate success. Here we begin to shed some light on the matter by showing that the SBC is in fact optimal even when the independence assumption is grossly violated, and is thus applicable to a much broader range of domains than previously thought. This is essentially due to the ....
Pazzani, M. (1995). Searching for attribute dependencies in Bayesian classifiers. In Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics, (pp. 424--429), Fort Lauderdale, FL. Society for Artificial Intelligence and Statistics.
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