D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....as majority voting can generally be applied to any type of classifier, while others rely on specific outputs, or specific interpretations of the output. For example, the confidence factors method relies on the interpretation of the outputs as the belief that the patterns belong to a given class [15, 10]. Averaging, on the other 2 f ind f comb Classifier 1 Classifier N Classifier m Feature Set 2 Feature Set 1 Feature Set M Raw Data from Observed Phenomenon Combiner Figure 1: Combining Strategy. The solid lines leading to f ind represent the decision of a specific classifier, while ....

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

....be applied to any type of classifier, while others rely on specific outputs, or specific interpretations of the output. For example, the confidence factors method found in machine learning literature relies on the interpretation of the outputs as the belief that a pattern belongs to a given class [22]. The rationale for averaging, on the other hand, is based on the result that the outputs of parametric classifiers that are trained to minimize a cross entropy or mean square error (MSE) function, given one of L desired output patterns, approximate the a posteriori probability densities of the ....

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

....literature, such as belief and evidence combining, forecasting, stacked generalization, and network ensembles. 22 2.6. 2 Belief and Evidence Combining Various methods of integrating evidence from disparate sources in machine reasoning have been developed and applied to knowledge based systems [12, 38, 39, 75, 105, 139, 140]. Although, in general, the scope and style of these works differ from the FFN combining approaches, there are certain concepts that have filtered through [167] The basis of the Dempster Shafer theory is in providing not only an answer to each query, but also a numeric value representing the ....

....to any type of classifier, while others rely on specific outputs, or specific interpretations of the output. For example, the confidence factors method found in machine learning literature relies on the interpretation of the outputs as the be 29 lief that a pattern belongs to a given class [75, 146]. The rationale for averaging, on the other hand, is based on the result that the outputs of parametric classifiers that are trained to minimize a cross entropy or mean square error (MSE) function, given one of L desired output patterns, approximate the a posteriori probability densities of the ....

D. Heckerman, Probabilistic interpretation for MYCIN's uncertainty factors, in Uncertainty in Artificial Intelligence, L. Kanal and J. Lemmer, eds., North-Holland, 1986, pp. 167--196.

....in expert systems. Certainty factors were introduced in the MYCIN expert system for reasoning in expert systems under uncertainty, and reflect the confidence in a given rule [22] The original method of rule combination in MYCIN was later expressed in a more probabilistic framework by Heckerman [13], and serves as the basis for the method proposed below: First, the outputs, which are in the range [0,1] are mapped into confidence factors (CFs) in the range [ 1,1] using a log transformation. Then, a Mycin type rule is used to combine the CFs for each class. The advantage of this combination ....

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

....in expert systems. Certainty factors were introduced in the MYCIN expert system for reasoning in expert systems under uncertainty, and reflect the confidence in a given rule [64] The original method of rule combination in MYCIN was later expressed in a more probabilistic framework by Heckerman [65], and serves as the basis for the method proposed below: First, the outputs, which are in the range [0,1] are mapped into certainty or confidence factors (CFs) in the range [ 1,1] using a log transformation. Then, a MYCIN type rule is used to combine the CFs for each class. The advantage of this ....

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

....in expert systems. Certainty factors were introduced in the MYCIN expert system for reasoning in expert systems under uncertainty, and reflect the confidence in a given rule [SB75] The original method of rule combination in MYCIN was later expressed in a more probabilistic framework by Heckerman [Hec86], and serves as the basis for the method proposed below: First, the outputs, which are in the range [0,1] are mapped into confidence factors (CFs) in the range [ 1,1] using a log transformation. Then, a Mycin type rule is used to combine the CFs for each class. The advantage of this combination ....

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

No context found.

D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors. In L.N Kanal and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167--196. North-Holland, 1986.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC