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results

- Go to itemAODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…
- Go to itemAODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…
- Go to itemA2DE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…
- Go to itemA2DE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…
- Go to itemClass for generating an alternating decision tree. The basic algorithm is based on: Freund, Y., Mason, L.: The alternating decisi…
- Go to itemAODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…
- Go to itemAODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification ti…
- Go to itemClass for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dra…
- Go to itemClass for boosting a nominal class classifier using the Adaboost M1 method Only nominal class problems can be tackled.Often drama…
- Go to itemMeta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left …
- Go to itemMeta classifier that enhances the performance of a regression base classifier Each iteration fits a model to the residuals left b…
- Go to itemEnables the aggregation of arbitrary distance measures using a Java snippet. The port number is used to refer to a value of a giv…
- Go to itemClass implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules…
- Go to itemClass implementing an Apriori-type algorithm Iteratively reduces the minimum support until it finds the required number of rules …
- Go to itemThe association rule learner* searches for frequent itemsets meeting the user-defined minimum support criterion and, optionally, …
- Go to itemThis rule learner* uses the Apriori (Agrawal et al. 1993) algorithm implemented by Christian Borgelt. The following description h…
- Go to itemDimensionality of training and test data is reduced by attribute selection before being passed on to a classifier. (based on WEKA…
- Go to itemDimensionality of training and test data is reduced by attribute selection before being passed on to a classifier (based on WEKA …
- Go to itemClass for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. F…
- Go to itemClass for building a best-first decision tree classifier This class uses binary split for both nominal and numeric attributes.For…