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Titlebook: Computer Recognition Systems; Proceedings of 4th I Marek Kurzyński,Edward Pucha?a,Andrzej ?o?nierek Conference proceedings 2005 Springer-Ve

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樓主: interleukins
21#
發(fā)表于 2025-3-25 06:23:24 | 只看該作者
22#
發(fā)表于 2025-3-25 08:30:54 | 只看該作者
23#
發(fā)表于 2025-3-25 13:57:59 | 只看該作者
Linear Ranked Regression - Designing Principles or later than the regarded one. A ranked regression task is aimed at designing such linear transformation of multivariate data sets on the line which preserves with the highest precision possible the ranked order. The convex and piecewise linear (CPL) criterion functions are used here for designing ranked linear models.
24#
發(fā)表于 2025-3-25 17:53:25 | 只看該作者
Conference proceedings 2005dzyna Castle (Poland), This conference is a continuation of a series of con- ferences on similar topics (KOSYR) organized each second year, since 1999, by the Chair of Systems and Computer Networks, Wroclaw University of Tech- nology. An increasing interest to those conferences paid not only by home
25#
發(fā)表于 2025-3-25 21:47:22 | 只看該作者
Neural Network-Based , Pattern Recognition — Part 2: Stability and Algorithmic Issuesated as the inputs. In this paper, we propose a new model for Pattern Recognition (PR), namely, one that involves Chaotic Neural Networks (CNNs). To achieve this, we enhance the basic model proposed by Adachi [.], referred to as . Neural Network (ACNN). Although the ACNN has been shown to be chaotic
26#
發(fā)表于 2025-3-26 00:18:36 | 只看該作者
27#
發(fā)表于 2025-3-26 06:20:59 | 只看該作者
28#
發(fā)表于 2025-3-26 08:34:22 | 只看該作者
29#
發(fā)表于 2025-3-26 15:39:00 | 只看該作者
30#
發(fā)表于 2025-3-26 19:59:32 | 只看該作者
Boosting the Fisher Linear Discriminant with Random Feature Subsetse and widely used classifier, boosting does not lead to a significant increase in accuracy. In this paper, a new method for adapting the FLD into the boosting framework is proposed. This method, the AdaBoost-RandomFeatureSubset-FLD (AB-RFS-FLD), uses a different, randomly chosen subset of features f
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