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NN
1998
Springer
177views Neural Networks» more  NN 1998»
15 years 10 days ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
107
Voted
ICIP
2006
IEEE
16 years 2 months ago
Using Non-Parametric Kernel to Segment and Smooth Images Simultaneously
Piecewise constant and piecewise smooth Mumford-Shah (MS) models have been widely studied and used for image segmentation. More complicated than piecewise constant MS, global Gaus...
Weihong Guo, Yunmei Chen
105
Voted
UAI
2004
15 years 2 months ago
Exponential Families for Conditional Random Fields
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decompo...
Yasemin Altun, Alexander J. Smola, Thomas Hofmann
KDD
2000
ACM
153views Data Mining» more  KDD 2000»
15 years 4 months ago
The generalized Bayesian committee machine
In this paper we introduce the Generalized Bayesian Committee Machine (GBCM) for applications with large data sets. In particular, the GBCM can be used in the context of kernel ba...
Volker Tresp
82
Voted
ISNN
2004
Springer
15 years 6 months ago
Geometric Interpretation of Nonlinear Approximation Capability for Feedforward Neural Networks
This paper presents a preliminary study on the nonlinear approximation capability of feedforward neural networks (FNNs) via a geometric approach. Three simplest FNNs with at most f...
Bao-Gang Hu, Hong-Jie Xing, Yujiu Yang