Titlebook: Artificial Neural Networks in Pattern Recognition; Second IAPR Workshop Friedhelm Schwenker,Simone Marinai Conference proceedings 2006 Spri

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书目名称Artificial Neural Networks in Pattern Recognition影响因子(影响力)




书目名称Artificial Neural Networks in Pattern Recognition影响因子(影响力)学科排名




书目名称Artificial Neural Networks in Pattern Recognition网络公开度




书目名称Artificial Neural Networks in Pattern Recognition网络公开度学科排名




书目名称Artificial Neural Networks in Pattern Recognition被引频次




书目名称Artificial Neural Networks in Pattern Recognition被引频次学科排名




书目名称Artificial Neural Networks in Pattern Recognition年度引用




书目名称Artificial Neural Networks in Pattern Recognition年度引用学科排名




书目名称Artificial Neural Networks in Pattern Recognition读者反馈




书目名称Artificial Neural Networks in Pattern Recognition读者反馈学科排名

tympanometry

Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognitionbased on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of

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Metamorphosis

Supervised Batch Neural Gasto learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervis

heterodox

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不法行为

A Study of the Robustness of KNN Classifiers Trained Using Soft Labelsing classes exist. In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes. Experiments were conducted

纠缠

抚育

A Local Tangent Space Alignment Based Transductive Classification Algorithmnal coordinates of high-dimensional data, and can also reconstruct high dimensional coordinates from embedding coordinates. But it ignores the label information conveyed by data samples, and can not be used for classification directly. In this paper, a transductive manifold classification method, ca

Ankylo-

Incremental Manifold Learning Via Tangent Space Alignment to extract the intrinsic characteristic of different type of high-dimensional data by performing nonlinear dimensionality reduction. Most of them operate in a “batch” mode and cannot be efficiently applied when data are collected sequentially. In this paper, we proposed an incremental version (ILTS