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Titlebook: Marketing Intelligent Systems Using Soft Computing; Managerial and Resea Jorge Casillas,Francisco J. Martínez-López Book 2010 Springer-Verl

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樓主: Coarctation
11#
發(fā)表于 2025-3-23 11:44:32 | 只看該作者
Using Data Fusion to Enrich Customer Databases with Survey Data for Database Marketingn be different though. In real world applications, the number of sources over which this information is fragmented can grow at an even faster rate, resulting in barriers to widespread application of data mining and missed business opportunities. Let us illustrate this paradox with a motivating examp
12#
發(fā)表于 2025-3-23 14:19:48 | 只看該作者
Collective Intelligence in Marketingl customers. With the recent adoption of large-scale, Internet-based information systems, marketing professionals now face large volumes of complex data, including detailed purchase and service transactions, social network links, click streams, blogs, comments and inquiries. While traditional market
13#
發(fā)表于 2025-3-23 19:28:19 | 只看該作者
Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computationrough the dependent variable of interest. While standard modeling approaches embody single performance objectives, actual marketing decisions often need consideration of multiple performance criteria. Multiple objective problems typically characterize a range of solutions, none of which dominate the
14#
發(fā)表于 2025-3-24 01:54:34 | 只看該作者
15#
發(fā)表于 2025-3-24 02:41:53 | 只看該作者
16#
發(fā)表于 2025-3-24 07:07:52 | 只看該作者
17#
發(fā)表于 2025-3-24 11:48:31 | 只看該作者
Direct Marketing Based on a Distributed Intelligent SystemDirect Marketing has benefited from computational methods to model consumer preferences, and many companies are beginning to explore this strategy to interact with customers. Nevertheless, it is still an open problem how to formulate, distribute and apply surveys to clients, and then gather their re
18#
發(fā)表于 2025-3-24 16:52:18 | 只看該作者
Direct Marketing Modeling Using Evolutionary Bayesian Network Learning Algorithmscovering models represented as Bayesian networks from incomplete databases in the presence of missing values. It combines an evolutionary algorithm with the traditional . algorithm to find better network structures in each iteration round. A data completing method is also presented for the convenie
19#
發(fā)表于 2025-3-24 22:48:36 | 只看該作者
20#
發(fā)表于 2025-3-24 23:22:00 | 只看該作者
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