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

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書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)




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書目名稱Artificial Neural Networks in Pattern Recognition被引頻次學(xué)科排名




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書目名稱Artificial Neural Networks in Pattern Recognition讀者反饋學(xué)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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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

糾纏

撫育

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