Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
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Các bài báo tiêu biểu
Signal reconstruction from sampled data using neural network
- Trang 707-715
For reconstructing a signal from sampling data, the method based on Shannon's
sampling theorem is usually employed. The reconstruction error appears when the
signal does not satisfy the Nyquist condition. This paper proposes a new
reconstruction method by using a linear perceptron and multilayer perceptron as
FIR filter. The perceptron, which has weights obtained by learning when adapting
the orig...
#Signal reconstruction #Neural networks #Image reconstruction #Sampling methods #Finite impulse response filter #Frequency #Adaptive filters #Image sampling #Information retrieval #Physics
Ký ức liên kết tạm thời và sự xấp xỉ hàm với bản đồ tự tổ chức Dịch bởi AI
- Trang 109-118
Chúng tôi đề xuất một kỹ thuật lập mô hình nơ-ron không giám sát, gọi là ký ức
liên kết tạm thời dạng vector (VQTAM), cho phép bản đồ tự tổ chức của Kohonen
(SOM) xấp xỉ các ánh xạ động lực phi tuyến trên toàn cầu. Phân tích lý thuyết về
phương pháp VQTAM cho thấy rằng độ sai lệch xấp xỉ giảm đi khi quá trình đào tạo
SOM diễn ra. SOM được so sánh với mạng MLP tiêu chuẩn và mạng RBF trong việc xác
...
#Ký ức liên kết #Xấp xỉ hàm #Mô hình hóa hệ thống sinh học #Thiết bị truyền động thủy lực #Hệ thống động lực phi tuyến #Mô hình toán học #Mô hình dự đoán #Hệ thống điều khiển phi tuyến #Mạng hàm cơ sở bán kính #Roentgenium
Simple algorithms for decorrelation-based blind source separation
- Trang 545-554
We present simple adaptive algorithms that perform blind source separation for
spatially-independent and temporally-correlated source signals. The proposed
algorithms are modified versions of a well-known natural gradient prewhitening
scheme, and the simplest version has almost the same complexity as this
prewhitening method. We provide a stationary point analysis of our schemes,
proving that the ...
#Decorrelation #Blind source separation #Source separation #Statistics #Iterative algorithms #Adaptive algorithm #Analysis of variance #Performance analysis #Gradient methods #Algorithm design and analysis
A comparative study of genetic sequence classification algorithms
- Trang 57-66
Classification of genetic sequence data available in public and private
databases is an important problem in using, understanding, retrieving, filtering
and correlating such large volumes of information. Although a significant amount
of research effort is being spent internationally on this problem, very few
studies exist that compare different classification approaches in terms of an
objective an...
#Genetics #Classification algorithms #Sequences #Databases #Clustering algorithms #Artificial neural networks #Information retrieval #Information filtering #Information filters #Frequency
Analog implementation for networks of integrate-and-fire neurons with adaptive local connectivity
- Trang 657-666
An analog VLSI implementation for pulse coupled neural networks of leakage free
integrate-and-fire neurons with adaptive connections is presented. Weight
adaptation is based on existing adaptation rules for image segmentation.
Although both integrate-and-fire neurons and adaptive weights can be
implementation only approximately, simulations have shown, that synchronization
properties of the origin...
#Neurons #Image segmentation #Frequency synchronization #Very large scale integration #Hardware #Neural networks #Robustness #Signal processing #Nearest neighbor searches #Information technology
Modified Kalman filter based method for training state-recurrent multilayer perceptrons
- Trang 219-228
Kalman filter based training algorithms for recurrent neural networks provide a
clever alternative to the standard backpropagation in time. However, these
algorithms do not take into account the optimization of the hidden state
variables of the recurrent network. In addition, their formulation requires
Jacobian evaluations over the entire network, adding to their computational
complexity. We propo...
#Multilayer perceptrons #Backpropagation algorithms #Recurrent neural networks #Signal processing algorithms #Computational complexity #Jacobian matrices #Kalman filters #Neural networks #Convergence #Neural engineering
Neural network-based segmentation of textures using Gabor features
- Trang 365-374
The effectiveness of Gabor filters for texture segmentation is well known. In
this paper, we propose a texture identification scheme, based on a neural
network (NN) using Gabor features. The features are derived from both the Gabor
cosine and sine filters. Through experiments, we demonstrate the effectiveness
of a NN based classifier using Gabor features for identifying textures in a
controlled en...
#Neural networks #Gabor filters #Frequency #Multilayer perceptrons #Multi-layer neural network #Image segmentation #Classification algorithms #Clustering algorithms #Robustness #Satellites
Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data
- Trang 77-86
This manuscript describes a combined approach of unsupervised clustering
followed by supervised learning that provides an efficient classification of
conditions in DNA array gene expression experiments (different cell lines
including some cancer types, in the cases shown). Firstly the dimensionality of
the dataset of gene expression profiles is reduced to a number of non-redundant
clusters of co-e...
#Supervised learning #Gene expression #DNA #Cancer #Neural networks #Principal component analysis #Clustering algorithms #Support vector machines #Support vector machine classification #Biotechnology
Metric-based model selection for time-series forecasting
- Trang 13-22
Metric-based methods, which use unlabeled data to detect gross differences in
behavior away from the training points, have recently been introduced for model
selection, often yielding very significant improvements over alternatives
(including cross-validation). We introduce extensions that take advantage of the
particular case of time-series data in which the task involves prediction with a
horizo...
#Predictive models #Linear regression #Input variables #Testing #Training data #Machine learning
Functional connectivity modelling in fMRI based on causal networks
- Trang 119-128
We apply the principle of causal networks to develop a new tool for connectivity
analysis in functional magnetic resonance imaging (fMRI). The connections
between active brain regions are modelled as causal relationships in a causal
network. The causal networks are based on the notion of d-separation in a
graph-theoretic context or, equivalently, on the notion of conditional
independence in a stat...
#Intelligent networks #Brain modeling #Numerical analysis #Mutual information #Equations #Entropy #Neuroimaging #Testing #Laboratories #Psychology