Sciweavers

ICANN
2005
Springer
13 years 10 months ago
Manifold Constrained Variational Mixtures
In many data mining applications, the data manifold is of lower dimension than the dimension of the input space. In this paper, it is proposed to take advantage of this additional ...
Cédric Archambeau, Michel Verleysen
ICANN
2005
Springer
13 years 10 months ago
Handwritten Digit Recognition with Nonlinear Fisher Discriminant Analysis
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonli...
Pietro Berkes
ICANN
2005
Springer
13 years 10 months ago
Classifying Unprompted Speech by Retraining LSTM Nets
Abstract. We apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unprompted speech- the German part of the VERBMOBIL corpus. Training first on a fra...
Nicole Beringer, Alex Graves, Florian Schiel, J&uu...
ICANN
2005
Springer
13 years 10 months ago
Robust Structural Modeling and Outlier Detection with GMDH-Type Polynomial Neural Networks
Abstract. The paper presents a new version of a GMDH type algorithm able to perform an automatic model structure synthesis, robust model parameter estimation and model validation i...
Tatyana I. Aksenova, Vladimir Volkovich, Alessandr...
ICANN
2005
Springer
13 years 10 months ago
Training of Support Vector Machines with Mahalanobis Kernels
Abstract. Radial basis function (RBF) kernels are widely used for support vector machines. But for model selection, we need to optimize the kernel parameter and the margin paramete...
Shigeo Abe
ICANN
2005
Springer
13 years 10 months ago
Principles of Employing a Self-organizing Map as a Frequent Itemset Miner
This work proposes a theoretical guideline in the specific area of Frequent Itemset Mining (FIM). It supports the hypothesis that the use of neural network technology for the prob...
Vicente O. Baez-Monroy, Simon O'Keefe
ICANN
2005
Springer
13 years 10 months ago
Smooth Bayesian Kernel Machines
Abstract. In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that t...
Rutger W. ter Borg, Léon J. M. Rothkrantz