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Titlebook: Computer Recognition Systems 4; Robert Burduk,Marek Kurzyński,Andrzej ?o?nierek Conference proceedings 2011 Springer Berlin Heidelberg 201

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樓主: risky-drinking
11#
發(fā)表于 2025-3-23 12:59:49 | 只看該作者
Evaluation of Reliability of a Decision-Making Process Based on Pattern Recognition, medicine, or biology, mechanisms assisting the decision-making process facilitate work significantly. One of the key issues is the reliability of made decisions. However, this issue is often passed over due to great increase in the amount of data processed by those systems, what forces the designers to focus on the systems’ efficiency.
12#
發(fā)表于 2025-3-23 17:21:31 | 只看該作者
13#
發(fā)表于 2025-3-23 20:22:00 | 只看該作者
14#
發(fā)表于 2025-3-24 01:27:35 | 只看該作者
Pose Invariant Face Recognition Method Using Stereo Visioninvisible due to occlusion, Eigenfaces recognition method is extended to operate on half-face images. Presented technique is verified experimentally to prove that it can be used to increase performance of image-based face recognition methods in applications where head pose of the subject is not controlled.
15#
發(fā)表于 2025-3-24 02:28:41 | 只看該作者
Subspace Algorithms for Face Verificationass (person). This work presents two such methods: one based on SDF and the other inspired by Clafic algorithm. In the experimental section they are compared to the two-class SVM on the realistic data set taken from CMU-PIE database. The results confirm the advantages of subspace approach.
16#
發(fā)表于 2025-3-24 06:49:28 | 只看該作者
17#
發(fā)表于 2025-3-24 13:48:21 | 只看該作者
18#
發(fā)表于 2025-3-24 16:19:16 | 只看該作者
Radial Basis Function Kernel Optimization for Pattern Classificationir application in kernel optimization is verified. Alternative evaluation measures that outperform presented methods are proposed.Optimization leveraging these measures results in parameters corresponding to the classifiers that achieve minimal error rate for . kernel.
19#
發(fā)表于 2025-3-24 22:42:20 | 只看該作者
Unified View of Decision Tree Learning Machines for the Purpose of Meta-learning the same environment. This is the start point for a manifold research in the area of DTs, which will bring advanced meta-learning algorithms providing new knowledge about DT induction and optimal DT models for many kinds of data.
20#
發(fā)表于 2025-3-25 02:13:52 | 只看該作者
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