易碎 发表于 2025-3-28 17:15:13
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 absoluPerigee 发表于 2025-3-28 21:08:04
http://reply.papertrans.cn/17/1627/162685/162685_42.pngcocoon 发表于 2025-3-29 01:04:25
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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 extensiofilicide 发表于 2025-3-29 10:43:20
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 withoAmorous 发表于 2025-3-29 11:47:16
http://reply.papertrans.cn/17/1627/162685/162685_46.pngLARK 发表于 2025-3-29 18:03:28
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公理 发表于 2025-3-29 23:43:44
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几何学家 发表于 2025-3-30 01:35:35
http://reply.papertrans.cn/17/1627/162685/162685_49.png白杨鱼 发表于 2025-3-30 07:08:35
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