3,642 research outputs found
Machine learning in spectral domain
Deep neural networks are usually trained in the space of the nodes, by
adjusting the weights of existing links via suitable optimization protocols. We
here propose a radically new approach which anchors the learning process to
reciprocal space. Specifically, the training acts on the spectral domain and
seeks to modify the eigenvectors and eigenvalues of transfer operators in
direct space. The proposed method is ductile and can be tailored to return
either linear or non linear classifiers. The performance are competitive with
standard schemes, while allowing for a significant reduction of the learning
parameter space. Spectral learning restricted to eigenvalues could be also
employed for pre-training of the deep neural network, in conjunction with
conventional machine-learning schemes. Further, it is surmised that the nested
indentation of eigenvectors that defines the core idea of spectral learning
could help understanding why deep networks work as well as they do
Representation of Functional Data in Neural Networks
Functional Data Analysis (FDA) is an extension of traditional data analysis
to functional data, for example spectra, temporal series, spatio-temporal
images, gesture recognition data, etc. Functional data are rarely known in
practice; usually a regular or irregular sampling is known. For this reason,
some processing is needed in order to benefit from the smooth character of
functional data in the analysis methods. This paper shows how to extend the
Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models
to functional data inputs, in particular when the latter are known through
lists of input-output pairs. Various possibilities for functional processing
are discussed, including the projection on smooth bases, Functional Principal
Component Analysis, functional centering and reduction, and the use of
differential operators. It is shown how to incorporate these functional
processing into the RBFN and MLP models. The functional approach is illustrated
on a benchmark of spectrometric data analysis.Comment: Also available online from:
http://www.sciencedirect.com/science/journal/0925231
A pragmatic approach to multi-class classification
We present a novel hierarchical approach to multi-class classification which
is generic in that it can be applied to different classification models (e.g.,
support vector machines, perceptrons), and makes no explicit assumptions about
the probabilistic structure of the problem as it is usually done in multi-class
classification. By adding a cascade of additional classifiers, each of which
receives the previous classifier's output in addition to regular input data,
the approach harnesses unused information that manifests itself in the form of,
e.g., correlations between predicted classes. Using multilayer perceptrons as a
classification model, we demonstrate the validity of this approach by testing
it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015,
Bruges, Belgium. 201
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
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