By Daniel Baier, Reinhold Decker, Lars Schmidt-Thieme

ISBN-10: 3540260072

ISBN-13: 9783540260073

ISBN-10: 3540283978

ISBN-13: 9783540283973

The quantity offers contemporary advances in info research and determination help and provides a precise evaluation at the interface among arithmetic, operations learn, information, machine technology, and administration technology. components that obtain huge awareness within the ebook are discrimination and clustering, multidimensional scaling, facts mining and selection help platforms in addition to purposes in advertising and company making plans. The reader will locate fabric on fresh technical and methodological advancements and numerous purposes demonstrating the usefulness of the newly constructed thoughts.

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Extra resources for Data Analysis and Decision Support (Studies in Classification, Data Analysis, and Knowledge Organization)

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Hashem (1999) has proposed the use of relative accuracy of component classifiers as weights in linear combinations of the members of an ensemble. Oza and Tumer (1999) developed a method named input decimation that eliminates features low correlated with the class. Zenobi and Cunnigham (2001) have used the hill-climbing search for feature subsets that is guided by diversity measure. Recently, Melville and Mooney (2003) have developed a method called DECORATE thsit reduces the classification error of an ensemble by increasing diversity.

1998): The random subspace method for constructing decision forests. , and DUIN, R. (2000): Is independence good for combining classifiers. In: Proceedingd of the 15th International Conference on Pattern Recognition, Barcelona, Spain, 168-171. KUNCHEVA, L. and WHITAKER, C. (2003): Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51, 181-207. M. G. (1997): Pruning adaptive boosting. In: Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann, San Mateo, 211-218.

The current description of the node A^ and rijsf the total number of units verifying the description of the node A^. A node N is considered as terminal (a leaf) if: the variation of its overflow AOR{N) overflow is less than a threshold fixed by the user or a default value say 10%; the difference between the discrimination before the cutting and the quality after the cutting AD{N) is less than a threshold fixed by the user or a default value say 10%; its size is not large enough (value < 2); it creates two empty son nodes (value < 1).

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Data Analysis and Decision Support (Studies in Classification, Data Analysis, and Knowledge Organization) by Daniel Baier, Reinhold Decker, Lars Schmidt-Thieme


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