Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing最新文献_第6页

Improving neural classifiers for ATR using a kernel method for generating synthetic training sets 利用核方法生成合成训练集改进ATR神经分类器

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030054

R. Gil-Pita, P. J. Amores, M. Rosa-Zurera, F. López-Ferreras

{"title":"Improving neural classifiers for ATR using a kernel method for generating synthetic training sets","authors":"R. Gil-Pita, P. J. Amores, M. Rosa-Zurera, F. López-Ferreras","doi":"10.1109/NNSP.2002.1030054","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030054","url":null,"abstract":"An important problem with the use of neural networks in HRR radar target classification is the difficulty in obtaining training data. Training sets are small because of this, making generalization to new data difficult. In order to improve generalization capability, synthetic radar targets are obtained using a novel kernel method for estimating the probability density function of each class of radar targets. Multivariate Gaussians whose parameters are a function of position and data distribution are used as kernels. In order to assess the accuracy of the estimate, the maximum a posteriori criterion has been used in radar target classification, and compared with the k-nearest-neighbour classifier. The proposed method performs better than the k-nearest-neighbour classifier, demonstrating the accuracy of the estimate. After that, the estimated probability density functions are used to classify the synthetic data in order to use a supervised training algorithm for neural networks. The obtained results show that neural networks perform better if this strategy is used to increase the number of training data. Furthermore, computational complexity is dramatically reduced compared with that of the k-nearest neighbour classifier.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"15 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128254244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A robust canonical correlation neural network 鲁棒典型相关神经网络

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030035

Zhenkun Gou, C. Fyfe

引用次数: 1

Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns 从皮质神经元放电模式估计手部运动轨迹的线性和非线性模型的输入-输出映射性能

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030025

Justin C. Sanchez, Sung-Phil Kim, Deniz Erdoğmuş, Y. Rao, J. Príncipe, J. Wessberg, M. Nicolelis

引用次数: 65

Modified Kalman filter based method for training state-recurrent multilayer perceptrons 基于改进卡尔曼滤波的状态递归多层感知器训练方法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030033

Deniz Erdoğmuş, Justin C. Sanchez, J. Príncipe

引用次数: 3

Efficient total least squares method for system modeling using minor component analysis 基于小分量分析的系统建模的有效总最小二乘方法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030037

Y. Rao, J. Príncipe

引用次数: 12

Analog implementation for networks of integrate-and-fire neurons with adaptive local connectivity 具有自适应局部连通性的整合-激发神经元网络的模拟实现

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030077

J. Schreiter, U. Ramacher, A. Heittmann, D. Matolin, R. Schüffny

引用次数: 10

Clustering of Sun exposure measurements 太阳照射测量的聚类

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030090

Anna Szymkowiak-Have, J. Larsen, L. K. Hansen, P. Philipsen, E. Thieden, H. Wulf

引用次数: 0

Metric-based model selection for time-series forecasting 基于度量的时间序列预测模型选择

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030013

Yoshua Bengio, Nicolas Chapados

引用次数: 1

Scaling of a length scale for regression and prediction 用于回归和预测的长度尺度的缩放

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030029

T. Aida

引用次数: 0

Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data 无监督降维，然后用感知器进行监督学习，改进了DNA微阵列基因表达数据的条件分类

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030019

L. Conde, Á. Mateos, Javier Herrero, J. Dopazo

{"title":"Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data","authors":"L. Conde, Á. Mateos, Javier Herrero, J. Dopazo","doi":"10.1109/NNSP.2002.1030019","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030019","url":null,"abstract":"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-expressing genes using an unsupervised clustering algorithm, the Self Organizing Tree Algorithm (SOTA), a hierarchical version of Self Organizing Maps (SOM). Then, the average values of these clusters are used for the training of a perception that produces a very efficient classification of the conditions. This way of reducing the dimensionality of the data set seems to perform better than other ones previously proposed such as PCA. In addition, the weights that connect the gene clusters to the different experimental conditions can be used to assess the relative importance of the genes in the definition of these classes. Finally, Gene Ontology (GO) terms are used to infer a possible biological role for these groups of genes and to asses the validity of the classification from a biological point of view.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"4 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120819729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9