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Titlebook: Soft Computing Applications in Business; Bhanu Prasad Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Markov model.audit.calculus.

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31#
發(fā)表于 2025-3-26 21:34:33 | 只看該作者
Structured Hidden Markov Models: A General Tool for Modeling Agent Behaviors,s specific regularities in the training sequences, an interesting feature in a knowledge extraction task. Several methods for incrementally constructing complex S-HMMs are also discussed, and examples of application to non trivial tasks of profiling are presented.
32#
發(fā)表于 2025-3-27 02:33:58 | 只看該作者
33#
發(fā)表于 2025-3-27 05:50:18 | 只看該作者
34#
發(fā)表于 2025-3-27 13:04:56 | 只看該作者
Vijay Kumar Mago,Bhanu Prasad,Ajay Bhatia,Anjali Mago
35#
發(fā)表于 2025-3-27 15:08:44 | 只看該作者
36#
發(fā)表于 2025-3-27 17:55:37 | 只看該作者
Predicting the Effects of Alternative Pricing Strategies in an Artificial Society Undergoing Technotting, it is unlikely to gain financial rewards. These results add to growing evidence that artificial society models may prove useful in improving our understanding of collective decision making in complex sociological, economic and business management situations.
37#
發(fā)表于 2025-3-28 00:17:18 | 只看該作者
Probabilistic Sales Forecasting for Small and Medium-Size Business Operations,stimates are adjusted over time, based on new developments in the sales opportunities. Furthermore, revenue from several types of sources can be combined in the forecast to cater for more complex business environments.
38#
發(fā)表于 2025-3-28 04:45:33 | 只看該作者
User Relevance Feedback Analysis in Text Information Retrieval: A Rough Set Approach,sitories, using an extended set of conceptual terms. This paper provides a detailed implementation of the proposed approach as well as its advantages in the context of user relevance feedback analysis based text information retrieval.
39#
發(fā)表于 2025-3-28 09:34:14 | 只看該作者
40#
發(fā)表于 2025-3-28 12:04:01 | 只看該作者
CBR Based Engine for Business Internal Control,k. Each one of the case-based reasoning systems that integrates the multi-agent system uses a different problem solving method in each step of the reasoning cycle: a Maximum Likelihood Hebbian learning-based method that automates the organization of cases and the retrieval phase, an Radial Based Fun
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