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Titlebook: Artificial Neural Networks in Pattern Recognition; 6th IAPR TC 3 Intern Neamat Gayar,Friedhelm Schwenker,Cheng Suen Conference proceedings

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樓主: LANK
41#
發(fā)表于 2025-3-28 17:15:13 | 只看該作者
Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRsthe discrete recognition rate. This leads to inferior feature selection results. To solve this problem, we propose using a least squares support vector regressor (LS SVR), instead of an LS support vector machine (LS SVM). We consider the labels (1/-1) as the targets of the LS SVR and the mean absolu
42#
發(fā)表于 2025-3-28 21:08:04 | 只看該作者
43#
發(fā)表于 2025-3-29 01:04:25 | 只看該作者
44#
發(fā)表于 2025-3-29 04:39:32 | 只看該作者
Hidden Markov Models Based on Generalized Dirichlet Mixtures for Proportional Data Modelingonal data modeling has been seldom mentioned in the literature. However, proportional data are a common way of representing large data in a compact fashion and often arise in pattern recognition applications frameworks. HMMs have been first developed for discrete and Gaussian data and their extensio
45#
發(fā)表于 2025-3-29 10:43:20 | 只看該作者
Majority-Class Aware Support Vector Domain Oversampling for Imbalanced Classification Problemsiption to the minority class but in contrast to many other algorithms, awareness of samples of the majority class is used to improve the estimation process. The majority samples are incorporated in the optimization procedure and the resulting domain descriptions are generally superior to those witho
46#
發(fā)表于 2025-3-29 11:47:16 | 只看該作者
47#
發(fā)表于 2025-3-29 18:03:28 | 只看該作者
Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streamsmble-based strategies have been proposed to preserve previously-acquired knowledge and reduce knowledge corruption, the fusion of multiple classifiers trained to represent different concepts can increase the uncertainty in prediction level, since only a sub-set of all classifier may be relevant. In
48#
發(fā)表于 2025-3-29 23:43:44 | 只看該作者
Combining Bipartite Graph Matching and Beam Search for Graph Edit Distance Approximation problem and can thus be solved in exponential time complexity only. A previously introduced approximation framework reduces the computation of GED to an instance of a linear sum assignment problem. Major benefit of this reduction is that an optimal assignment of nodes (including local structures) c
49#
發(fā)表于 2025-3-30 01:35:35 | 只看該作者
50#
發(fā)表于 2025-3-30 07:08:35 | 只看該作者
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